In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. We further evaluate the degree to which theory-driven phonological features are encoded in the latent bit patterns, finding that some (e.g. [  approximant]), are well represented by the network in both languages, while others (e.g. [  spread glottis]) are less so. Together, these findings suggest that many reliable cues to phonemic structure are immediately available to infants from bottom-up perceptual characteristics alone, but that these cues must eventually be supplemented by top-down lexical and phonotactic information to achieve adult-like phone discrimination. Our results also suggest differences in degree of perceptual availability between features, yielding testable predictions as to which features might depend more or less heavily on top-down cues during child language acquisition. Distinctive features like [  voice] and [  sonorant] have been a core construct of phonological theory for many decades (Trubetskoy, 1939; Jakobson et al., 1951; Chomsky and Halle, 1968; Clements, 1985). They have been used in automatic speech recognition (Livescu and Glass, 2004), and psycholinguistic evidence suggests that they are cognitively available during language acquisition (Kuhl, 1980; White and Morgan, 2008). Nonetheless, distinctive features are not directly observed by humans; they are abstractions that must be inferred from dense perceptual information (sound waves) during language acquisition and comprehension, which raises questions about how they are learned and recognized. In adults, phonological comprehension is aided by top-down lexical and phonotactic (i.e. sound sequencing) constraints. For example, the classic phonemic restoration effect (Warren, 1970) shows that adults infer missing phonemes from context with such ease that they often fail to notice when acoustic cues to phone identity are erased. However, infants first learning their phonemic categories have not yet acquired reliable top-down lexical and phonotactic models and must rely more heavily on bottom-up perceptual information. To a learner faced with the immense challenge of discovering structure in dense perceptual input, do theory-driven phonological features stand out or are they swamped by noise? In this paper, we address this question using an unsupervised computational acquisition model. Previous models of phonological category induction have emphasized the importance of top-down information (information about the contexts in which phonemes occur) (Peperkamp et al., 2006; Swingley, 2009; Feldman et al., 2009a, 2013a,b; Moreton and Pater, 2012a,b; Martin et al., 2013; Pater and Moreton, 2014; Frank et al., 2014; Doyle et al., 2014; Doyle and Levy, 2016). But to prevent the acquisition process from being circular, the learner cannot operate solely on top-down information  the acoustic signal must provide some evidence for the phonemic categories. We hypothesize that the same must be true for at least some phonological features (e.g. [  nasal], [  lateral]), but previous work on unsupervised speech processing has inferred phonological structure from spoken utterances using either (1) discrete transition-based architectures (Varadarajan et al., 2008; Jansen and Church, 2011; Lee and Glass, 2012), which do attempt to discover featurally-related natural classes, or (2) continuous deep neural (Kamper et al., 2015, 2017a; Renshaw et al., 2015) architectures, whose internal representations are difficult to interpret. Furthermore, these approaches do not separate the contributions of top-down sequential information from bottom-up acoustic properties of segments, making it difficult to assess the relative importance of these information sources throughout the acquisition process. By contrast, our model attends exclusively to phone-internal acoustic patterns using a deep neural autoencoder with a discrete embedding space composed of binary stochastic neurons (BSNs) (Rosenblatt, 1958; Hinton, 2012; Bengio et al., 2013; Courbariaux et al., 2016). BSNs allow us to exploit (1) the interpretability of discrete representations, (2) the decomposability of phone segments into phonological features, and (3) and the power of deep neural function approximators to relate percepts and their representations. Since every token is labeled with a binary latent code, it is possible to evaluate the model's recovery not only of phonological categories but also of phonological features. Featural representations can encode distributional facts about which processes apply to which classes of sounds in ways that cross-cut the phonological space, rather than simply grouping each segment with a set of similar neighbors (LeCun et al., 2015). By focusing on the acoustic properties of sounds themselves rather than their sequencing in context, our model enables exploration of two questions about the data available to young learners whose training signal must primarily be extracted from bottom-up perceptual information: (1) to what extent can phoneme categories emerge from a drive to model auditory percepts, and (2) how perceptually available are theory-driven phonological features (that is, how easily can they be extracted directly from low-level acoustic percepts)? Our results show (a) that phonemic categories emerge naturally but imperfectly from perceptual reconstruction and (b) that theory-driven features differ in their degree of perceptual availability. Together, these findings suggest that many reliable cues to phonemic structure are immediately available to infants from bottom-up perceptual characteristics alone, but that these cues may eventually need to be supplemented by top-down lexical and phonotactic information to achieve adult-like phone discrimination (Feldman et al., 2013a; Pater and Moreton, 2014). Our findings also suggest hypotheses as to precisely which kinds of phonological features are more or less perceptually available and therefore might depend more or less heavily on top-down cues for acquisition. Such differences might suggest relative timelines at which different features might be appropriated in support of phonemic, phonotactic, and lexical generalization, providing a rich set of testable hypotheses about child language acquisition. The present paper has a strong connection to re-cent work on unsupervised speech processing, especially the Zerospeech 2015 (Versteegh et al., 2015) and 2017 (Dunbar et al., 2017) shared tasks. Participating systems (Badino et al., 2015; Renshaw et al., 2015; Agenbag and Niesler, 2015; Chen et al., 2015; Baljekar et al., 2015; Rasanen et al., 2015; Lyzinski et al., 2015; Zeghidour et al., 2016; Heck et al., 2016; Srivastava and Shrivas-tava, 2016; Kamper et al., 2017b; Chen et al., 2017; Yuan et al., 2017; Heck et al., 2017; Shi-bata et al., 2017; Ansari et al., 2017a,b) perform unsupervised ABX discrimination and/or spoken term discovery on the basis of unlabeled speech alone. The design and evaluation of these and related systems (Kamper et al., 2015, 2017a; Elsner and Shain, 2017; Rasanen et al., 2018) are oriented toward word-level modeling. As such, our focus on the perceptual availability of phonological features is orthogonal to but complementary with this line of research. Since distinctive features are important for indexing lexical contrasts, especially between highly confusable words (e.g. onset voicing alone distinguishes sap and zap in English), studying the perceptual availability of distinctive features to an unsupervised learner may help improve the design and analysis of low-resource speech processing systems. To our knowledge, the task most closely related to the current paper is unsupervised phone discovery. Some studies in this tradition segment speech into phone-like units without clustering them (Dusan and Rabiner, 2006; Qiao et al., 2008), while others cluster small subsets of pre-segmented sounds (usually vowels) using parametric models (mixture-of-Gaussians) (Vallabha et al., 2007; Feldman et al., 2013a; Antetomaso et al., 2017). Further work combines these tasks and extends the approach to cover the entire acoustic space (Lee and Glass, 2012). However, for a variety of reasons, the Lee and Glass (2012) model does not straightforwardly support evaluation of the perceptual availability of phonological features. First, they do not quantitatively evaluate the discovered phoneme clusters. Second, the model incorporates phonotactics through transition probabilities, making it difficult to disentangle the contributions of top-down and bottom-up information to the learning process. Third, the clustering model is not feature-based, but instead consists of atomic categories, each defining a distinct generative process for acoustics. This design is at odds with the widely held view in linguistic theory that phonemes are not inscrutable atoms of the phonological grammar, but instead labels for bundles of features that define natural classes (Clements, 1985). Our approach is therefore more appropriate to the question at hand. There is a great deal of evidence that many phonological contrasts are perceptually available from a very early stage (Eimas et al., 1971; Moffitt, 1971; Trehub, 1973; Jusczyk and Derrah, 1987; Eimas et al., 1987). However, studies of infant phone discrimination typically use carefully-enunciated laboratory stimuli, which have been shown to be substantially easier to discriminate than phones in naturalistic utterances (Feldman et al., 2013a; Antetomaso et al., 2017). It is thus likely that inferring phone categories from acoustic evidence is a persistently challenging task, and studies have found language-specific tuning of the speech perception system from fetal stages (Moon et al., 2013) through the first year (Kuhl et al., 1992; Werker and Tees, 1984) and even all the way into the preteen years (Hazan and Barrett, 2000). Experiments show that these contrasts are expressed, not simply as oppositions between particular categories, but as a featural system, even in early infancy. Evidence of featural effects has been found in the phone discrimination patterns of both adults (Chladkova et al., 2015) and infants (Kuhl, 1980; Hillenbrand, 1985; White and Morgan, 2008). Studies have also shown that infants generalize new distinctions along featural dimensions (Maye et al., 2008b; Cristi`a et al., 2011). Given infants' early detection and use of some featural contrasts, we hypothesize that there is strong evidence in the acoustic signal for these distinctions, which may then bootstrap the acquisition of phonotactic and lexical patterns (Beckman and Edwards, 2000). Experiments also suggest asymmetries in the perceptual availability of features. For example, a consonant-vowel distinction appears to be an important early foothold in phonology acquisition: vowel/consonant discrimination emerges early in infant speech processing (Dehaene-Lambertz and Dehaene, 1994), language-specificity in perception follows different timecourses for consonants (Werker and Tees, 1984) and vowels (Kuhl et al., 1992), and vowels and consonants play distinct roles in lexical access vs. rule discovery in children (Nazzi, 2005; Pons and Toro, 2010; Hochmann et al., 2011). Young infants have also been shown to be sensitive to voicing contrasts (Lasky et al., 1975; Aslin et al., 1981; Maye et al., 2008b). Features that distinguish consonant-like from vowel-like segments or voiced from unvoiced segments may thus be highly available to young learners. Infants struggle by comparison with other kinds of phone discrimination tasks, including certain stop-fricative contrasts (Polka et al., 2001) and certain place distinctions within nasal (Narayan et al., 2010) and sibilant (Nit-trouer, 2001; Cristi`a et al., 2011) segments. Even adults struggle with fricative place discrimination from strictly acoustic cues (McGuire and Babel, 2012). Similar asymmetries emerge from our unsupervised learner, as shown in Section 4.2. Our computational acquisition model complements this experimental research in several ways. First, its internal representations, unlike those of human infants, are open to detailed analysis, even when exposed to naturalistic language stimuli. Second, we can perform cross-linguistic comparisons using readily available corpora without requiring access to a pool of human subjects in each language community. Third, our model provides global and graded quantification of the perceptual availability of distinctive features in natural speech, permitting us to explore relationships between features in a way that is difficult to do through experiments on infants, which are generally constrained to same-different contrasts over a small set of manipulations. The reconstruction objective used here is not merely a convenient supervision signal. There is reason to believe that people actively model their perceptual worlds (Mamassian et al., 2002; Feldman, 2012; Singer et al., 2018; Yan et al., 2018), and autoassociative structures have been found in several brain areas (Treves and Rolls, 1991; Rolls and Treves, 1998). There is also evidence that phonetic comprehension and production can be acquired symbiotically through a sensorimotor loop relating acoustic perception and articulator movements (Houde and Jordan, 1998; Fadiga et al., 2002; Watkins et al., 2003; Wilson et al., 2004; Pulvermuller et al., 2006; Kroger et al., 2009; Bolhuis et al., 2010; Kroger and Cao, 2015; Bekolay, 2016). Finally, evidence suggests that working memory limitations impose compression pressures on the perceptual system that favor sparse representations of dense acoustic percepts (Baddeley and Hitch, 1974) and may guide infant language acquisition (Baddeley et al., 1998; Elsner and Shain, 2017). It is thus reasonable to suppose that perceptual reconstruction  such as that implemented by an autoencoder architecture  is immediately available as a learning signal to infants who still lack reliable guidance from phonotactics or the lexicon. Our use of BSNs follows the spirit of the earliest work on artificial neural networks (Rosen-blatt, 1958). Rosenblatt's perceptron was designed to study learning and decision-making in the brain and therefore used binary neurons to model the discrete firing behavior of their biological counterparts. This tradition has been replaced in deep learning research with differentiable activation functions that support supervised learning through backpropagation of error but are less biologically plausible. Our work takes advantage of the development of effective estimators for the gradients of discrete neurons (Williams, 1992; Hinton, 2012; Bengio et al., 2013; Courbariaux et al., 2016; Chung et al., 2017) to wed these two traditions, exploiting BSNs to encode the learner's latent representation of auditory percepts and deep networks to map between percepts and their latent representations. In addition to the greater similarity of BSNs to biological neurons, the use of discrete featural representations is motivated by experimental evidence that human phone perception (including that of infants) is both featural (White and Morgan, 2008; Chladkova et al., 2015) and categorical (Liberman et al., 1961; Eimas et al., 1987; Harnad, 2003; Feldman et al., 2009b). Experiments reported here use an 8-bit binary segment encoding. Eight bits is the the lower bound on binary encodings that are sufficiently expressive to capture all segmental contrasts in any known language (Mielke, 2009). Although theory-driven taxonomies generally contain more than eight distinctive features, these taxonomies are known to be highly redundant (Cherry et al., 1953). For example, the phonological featurization of the Xitsonga segments analyzed in our experiments contains 26 theory-driven features (Hayes, 2011; Hall et al., 2016), yielding up to 2 26 = 67108864 distinct segment categories, far more than the number of known segment types in Xitsonga or even the number of training instances in our data. By entailment, any representation that can identify all segment types in a language can also identify all featural contrasts that discriminate those types, regardless of how the feature space is factored. For this reason, we consider a phonological feature to be represented if it can be detected by an arbitrary function of the latent bits (Section 4.2), without assuming that the true and discovered feature spaces will factor identically. Our study shares an interest in phonological features with previous work in automatic speech recognition attempting to discover mappings between acoustics and hand-labeled featural representations (Liu, 1996; Bitar and Espy-Wilson, 1996; Frankel and King, 2001; Kirchhoff et al., 2002; Livescu and Glass, 2004; Mitra et al., 2011, inter alia ). While these results provide evidence that such a mapping is indeed learnable in an oracle setting, they rely on a supervision signal (direct annotation of the target representations) to which children do not have access. Our unsupervised approach measures perceptual availability of features in a more realistic learning scenario. The simulated learner used in this study is a deep neural autoencoder with an 8-bit layer of BSNs as its principle information bottleneck, depicted in Figure 1. The model processes a given phone segment by encoding the segment's acoustic informa-Acoustics E 1 E 2 ... tion into a bit pattern and then reconstructing the acoustic information from the encoded bit pattern. It is thus incentivized to use the latent bits in a systematic featural manner, encoding similar segments in similar ways. The encoder and decoder are both deep feed-foward residual networks (He et al., 2016). 1 To enable feedforward autoencoding of sequential data, phone segments are clipped at 50 timesteps (500ms), providing complete coverage of over 99% of the phone segments in each corpus. Given F -dimensional input acoustic frames and a maximum input length of M timesteps, the weight matrix of each encoder layer is  RFM  FM except the final layer (  RFM  8 ). Given R -dimensional reconstructed acoustic frames and a maximum output length of N timesteps, the weight matrix of each decoder layer is  RRN  RN except the first layer (  R 8  RN ). Both the encoder and decoder contain initial and final dense transformation layers, with three residual layers in between. Each residual layer contains two dense layers. All internal layers use tanh activations and are batch-normalized with a decay rate of 0.9 (Ioffe and Szegedy, 2015). Given that the capacity for speaker adaptation  short-term accommodation of idiosyncrasies in individuals' productions  has been shown for 1 Feedforward networks are used both for computational reasons and because they dramatically outperformed recurrent networks in initial experiments, especially when RNN's were used for decoding. We hypothesize that this is due to the lack of direct access to the encoder timesteps, such as that permitted by sequence to sequence models with attention (Bahdanau et al., 2015). Attention is not viable for our goals because it defeats the purposes of an autoencoder by allowing the decoder to bypass the encoder's latent representation. both adults (Clarke and Garrett, 2004; Maye et al., 2008a) and children (Kuhl, 1979; van Heugten and Johnson, 2014), we equip the models with a 16-dimensional speaker embedding, which is concatenated both to the acoustic input frames and to the latent bit vector. Each BSN of the latent encoding is associated with a firing probability  [0 , 1] parameterized by the encoder network. The neural activation can be discretized either deterministically or by sampling. The use of BSNs to encode segments is a problem for gradient-based optimization since it introduces a non-differentiable discrete decision into the network's latent structure. We overcome this problem by approximating missing gradients using the straight-through estimator (Hin-ton, 2012; Bengio et al., 2013; Courbariaux et al., 2016) with slope-annealing (Chung et al., 2017). Slope annealing multiplies the pre-activations a by a monotonically increasing function of the training iteration t , incrementally decreasing the bias of the straight-through estimator. We use the following annealing function: a  a (1 + 0 . 1 t ) We discretize the latent dimensions using Bernoulli sampling during training and threshold-ing at 0.5 during evaluation. The models are implemented in Tensorflow (Abadi et al., 2015) and optimized using Adam (Kingma and Ba, 2014) for 150 training epochs with a constant learning rate of 0.001. The source code is available at https://github.com/ coryshain/dnnseg . We apply our model to the Xitsonga and English speech data from the Zerospeech 2015 shared task. The Xitsonga data are drawn from the NCHLT corpus (De Vries et al., 2014) and contain 2h29m07s of read speech from 24 speakers. The English data are drawn from the Buckeye Corpus (Pitt et al., 2005) and contain 4h59m05s of conversational speech from 12 speakers. While neither of these corpora represent child-directed speech, they both consist of fluently produced word tokens in context, rather than isolated productions as in many previous laboratory studies with infants (Eimas et al., 1971; Werker and Tees, 1984; Kuhl et al., 1992, inter alia ). We pre-segment the audio files using time-aligned phone transcriptions pro-Xitsonga English Model H C V H C V Baseline 0.023 0.013 0.016 0.006 0.004 0.005 Sigmoid 0.281 0.191 0.227 0.246 0.166 0.198 Sigmoid+Speaker 0.302 0.185 0.230 0.205 0.180 0.192 BSN 0.360 0.206 0.262 0.240 0.161 0.193 Our model (BSN+Speaker) 0.462 0.268 0.339 0.270 0.180 0.216 Table 1: Phone clustering scores. vided in the challenge repository. The gold segment labels are used in clustering evaluation metrics, but the unsupervised learner never has access to them. Data selection criteria and annotation procedures are are described in more detail in Ver-steegh et al. (2015). Prior to fitting, we apply a standard spectral preprocessing pipeline from automatic speech recognition: raw acoustic signals are converted into 13-dimensional vectors of Mel frequency cepstral co-efficients (MFCCs) (Mermelstein, 1976) with first and second order deltas, yielding 39-dimensional frames sequenced in time. Each frame covers 25ms of speech, and frames are spaced 10ms apart. The deltas are used by the encoder but stripped from the reconstruction targets. Following preceding work showing improved unsupervised clustering when segments are given fixed-dimensional acoustic representations, thus abstracting away from the variable temporal dilation in natural speech (Kamper et al., 2017a,b), we resample all reconstruction targets to a length of 25 frames. This pipeline instantiates some standard assumptions about the perceptual representations underlying human speech processing. Alternative representations  for instance, articulatory representations (Liu, 1996; Frankel and King, 2001; Kirchhoff et al., 2002; Livescu and Glass, 2004) or other spectral transforms (Zwicker, 1961; Makhoul, 1975; Hermansky, 1990; Hermansky et al., 1991; Coifman and Wickerhauser, 1992; Shao et al., 2009)  have been proposed as alternatives to MFCCs. Our results concerning perceptual availability are of course tied to our input representation, since phenomena that are poorly distinguished by MFCCs have less effect on our autoencoder loss function. Nonetheless, MFCCs are known to produce high-quality supervised speech recognizers (Zheng et al., 2001; Hinton et al., 2012), and we therefore leave optimization of the representation of speech features to future work. The first research question posed in the introduction was to what extent theory-driven phoneme categories emerge from a drive to model auditory percepts. We explore this question by evaluating the degree of correspondence between the autoencoder hidden states and the gold phone labels. Table 1 reports learning outcomes using the information theoretic measures homogeneity (H), completeness (C), and V-measure (V) for unsupervised cluster evaluation (Rosenberg and Hirschberg, 2007). All three metrics range over the interval [0 , 1] , with 1 indexing perfect performance. As shown in the table, our model yields dramatically better clustering performance than a random baseline that uniformly draws cluster IDs from a pool of 256 categories: we obtain 2118% and 4500% relative V-measure improvements in Xitsonga and English, respectively. At the same time, clustering performance is far from perfect. This result indicates that perceptual modeling  an immediately-available learning signal in infant language acquisition  both (1) drives the learner a long way toward phoneme acquisition, and (2) is insufficient to fully identify phone categories in our learners. One likely explanation for the latter is evidence from cognitive science that phonotactic and lexical information (to which our learners do not have access) supplement perception as the acquisition process unfolds (Feldman et al., 2013a; Pater and Moreton, 2014). The middle rows of Table 1 show ablation results from using non-discrete sigmoid neurons rather than BSNs in the encoding layer ( Sigmoid vs. BSN ) 2 and/or removing the speaker adaptation feature (i.e. removing speaker embeddings). As shown, the classification performance of our model benefits substantially from the use of BSN encodings with speaker adaptation, especially on Xitsonga. Note that the reconstruction losses of the sigmoid encoders are better than those of the BSN encoders despite their degraded classification performance. This is to be expected: sigmoid neurons have greater representational capacity than binary neurons, since they can encode information through continuous gradations. They are therefore more capable of memorizing idiosyncratic properties of the input and are less incentivized to discover generalizable latent classes. The ablation results thus suggest that speaker adaptation and categorical perception support the discovery of linguistically relevant abstractions. The second research question posed in the introduction was to what extent distinctive features differ in perceptual availability. We explore this question in two ways. First, we qualitatively assess the linguistic plausibility of the natural clustering in the latent 2 To obtain class labels from the sigmoid encoder, we rounded the activations. Rounding was only used for evaluation and had no impact on the fitting procedure. bits. Figure 2 visualizes this clustering based on correlations between the average of the bit patterns across all instances of each gold phone type for both datasets. If the unsupervised classifier ignored phonological structure altogether, the plots would be roughly uniform in color, and if the unsupervised classifier perfectly identified phonemes, the plots would consist entirely of fully light or fully dark cells, with unique bit patterns associated with each phone type. As shown, the reality falls in between: while the visualized clas-sifications are far from perfect, they nonetheless contain a great deal of structure and suggest the presence of rough natural classes in both languages, especially of affricates, nasals, sibilants, and approximants. Our learners also replicate infants' difficulty in discriminating some nasal and fricative place features (Polka et al., 2001; Nittrouer, 2001; Narayan et al., 2010), assigning highly similar representations to many subtypes of nasals and fricatives across places of articulation (see e.g. similar mean bit patterns of /n/ vs. / n / and /s/ vs. / s / in both languages). Second, we quantitatively evaluate the degree to which theory-driven features like [  voice] are recoverable from the network's latent representations. To do so, we map gold phone labels into binary distinctive feature clusters from Hayes (2011) using Phonological CorpusTools (Hall et al., 2016). One possible form of analysis would be to search for individual correspondences between distinctive features and the model's latent dimensions. However, this is likely to underestimate the degree of feature learning because the deep decoder can learn arbitrary logics on the latent bit patterns, a necessary property for fitting complex non-linear mappings from latent features to acoustics. We instead evaluate distinctive feature discovery by fitting random forest classifiers that predict theory-driven features using the latent bit patterns as inputs. We can then use classifier performance to assess the degree to which a given distinctive feature can be recovered by a logical statement on the network's latent bits. The classifiers were fitted using 5-fold cross-validation in Scikit-learn (Pedregosa et al., 2011) with 100 estimators, balanced class weighting, and an entropy-based split criterion. Results are given in Tables 2 and 3. As shown, (1) there are large differences in perceptual availability between features, and (2) relative avail-Feature P R F voice 0.9767 0.9033 0.9386 sonorant 0.9249 0.9085 0.9166 continuant 0.9492 0.7936 0.8645 consonantal 0.8314 0.8915 0.8604 approximant 0.8998 0.8192 0.8576 syllabic 0.8278 0.8523 0.8398 dorsal 0.8935 0.7703 0.8273 strident 0.6991 0.9594 0.8089 low 0.7175 0.8978 0.7976 front 0.6590 0.8101 0.7268 high 0.5875 0.7882 0.6732 back 0.5352 0.8527 0.6577 round 0.5332 0.8551 0.6568 labial 0.5669 0.7725 0.6539 coronal 0.5382 0.8301 0.6530 tense 0.5208 0.8115 0.6344 delayed release 0.5468 0.7226 0.6225 anterior 0.4078 0.8355 0.5481 nasal 0.3635 0.8796 0.5144 distributed 0.2459 0.8537 0.3819 constricted glottis 0.1762 0.9007 0.2948 lateral 0.1536 0.8062 0.2581 labiodental 0.0934 0.7980 0.1672 trill 0.0809 0.7401 0.1458 spread glottis 0.0671 0.5856 0.1204 implosive 0.0041 0.4041 0.0081 Table 2: Perceptual availability by feature in Xitsonga Feature P R F voice 0.9244 0.8567 0.8893 sonorant 0.8544 0.8862 0.8700 approximant 0.8005 0.8370 0.8183 continuant 0.8577 0.7669 0.8098 consonantal 0.8249 0.7357 0.7777 syllabic 0.6624 0.8426 0.7417 dorsal 0.7046 0.7114 0.7080 strident 0.5505 0.9027 0.6839 coronal 0.5758 0.7066 0.6345 anterior 0.5251 0.7280 0.6101 delayed release 0.4413 0.7374 0.5521 front 0.4322 0.7407 0.5459 high 0.3841 0.6931 0.4943 tense 0.3275 0.7101 0.4483 back 0.3128 0.7504 0.4416 nasal 0.2796 0.7544 0.4080 labial 0.2541 0.7077 0.3739 low 0.2410 0.7787 0.3680 distributed 0.2203 0.6881 0.3337 diphthong 0.2039 0.8051 0.3254 round 0.1665 0.7012 0.2692 lateral 0.1484 0.8333 0.2519 labiodental 0.0787 0.6756 0.1410 spread glottis 0.0377 0.6683 0.0714 Table 3: Perceptual availability by feature in English ability of features is remarkably consistent between these unrelated languages, suggesting that the models are tapping into generalized perceptual patterns. The best-learned feature in both languages is [  voice], which is consistent with early evidence of voicing sensitivity in infants (see Section 2.2). Below this, the features [  sonorant], [  continuant], [  consonantal], [  approximant], and [  syllabic] are faithfully recovered in both languages. All of these features distinguish prototypical consonants from prototypical vowels but differ in their treatment of edge cases like nasals, liquids, and glides. Thus, similarly to the infant subjects discussed in Section 2.2, the model finds the consonant-vowel contrast to be highly available. Like human infants, our computational learner finds certain consonantal place and manner features relatively more difficult, although the features [  dorsal], [  coronal], [  strident] and [  delayed release] are also fairly well recovered in both languages. By contrast, both models poorly capture features like [  lateral], [  labiodental], [  distributed], [  nasal], [  constricted glottis], [  spread glottis], and [  implosive], 3 suggesting that these features are more difficult to discover bottom-up and may 3 Delayed release: affricates, constricted glottis: ejectives; spread glottis: glottal frication (e.g. aspirated stops). therefore be more dependent on phonotactic and lexical constraints for acquisition. 4 This finding aligns with the acquisition literature in suggesting that there may be substantial differences in perceptual availability between different place and manner features (see Section 2.2). In addition to these cross-linguistic similarities, the models also reveal important differences between Xitsonga and English. For example, the two languages differ in the relative availability of features that distinguish vowels vs. features that distinguish consonants. In English, vowel features like [  front], [  high], and [  back] are substantially less well learned than consonant features like [  coronal], [  anterior], and [  delayed re-lease], while the opposite holds in Xitsonga. We hypothesize that this is due to the fact that there are more vowels and fewer consonants in English than in Xitsonga: having fewer distinctions might reduce the degree of crowding in the articulatory space, increasing perceptual contrast between phone types (Liljencrants and Lindblom, 1972). 4 Note that we are not suggesting that e.g. [  spread glottis] cannot be detected in speech. Our claim is rather that acoustic cues to [  spread glottis] are less pronounced and/or less reliable than cues to e.g. [  voice] and therefore perhaps more difficult to exploit in early infancy, since our autoencoder model does not find them particularly useful for perceptual reconstruction. Finally, note that the cluster maps in Figure 2 and the feature recovery data in Tables 2 and 3 provide complementary perspectives on the learned representations. For example, it may at first seem surprising that the feature [  nasal] is recovered relatively poorly in both languages, given that nasals are well clustered in Figure 2. This discrepancy indicates that nasal segments are represented similarly to each other but also similarly enough to other segments that they are not reliably differentiated as a class. Conversely, the voicing feature is well recovered in both languages despite the lack of a visible cluster of voiced segments. This indicates that voicing is reliably encoded in the latent bits, even if the representation as a whole is dominated by other kinds of information. In this paper, we used binary stochastic neural autoencoders to explore the perceptual availability of (1) theory-driven phonemic categories and (2) theory-driven phonological features, based only on the acoustic properties of segments. We found that phonemic categories exert substantial influence on a learner driven to model its auditory percepts, but that additional information  especially phonotactic and lexical (Feldman et al., 2013a)  is likely necessary for full adult-like phone discrimination. We also found asymmetries in the perceptual availability of phonological features like [  voice] and [  nasal] and showed that these asymmetries reflect attested patterns of infant phone discrimination. Our model both replicates broad trends in the child acquisition literature (successful consonant-vowel and voicing discrimination, relatively less successful discrimination of various place and manner features) and sheds new light on potential relationships between auditory perception and language acquisition: the overall cline of perceptual availability revealed by the model in Tables 2 and 3 suggests a range of testable hypotheses about the role of perception in infant speech processing. The authors would like to thank the anonymous reviewers for their helpful comments. This work was supported by National Science Foundation grant #1422987 to ME. All views expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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