declare-lab/segue-w2v2-base

Repository: https://github.com/declare-lab/segue

Paper: https://arxiv.org/abs/2305.12301

SEGUE is a pre-training approach for sequence-level spoken language understanding (SLU) tasks. We use knowledge distillation on a parallel speech-text corpus (e.g. an ASR corpus) to distil language understanding knowledge from a textual sentence embedder to a pre-trained speech encoder. SEGUE applied to Wav2Vec 2.0 improves performance for many SLU tasks, including intent classification / slot-filling, spoken sentiment analysis, and spoken emotion classification. These improvements were observed in both fine-tuned and non-fine-tuned settings, as well as few-shot settings.

How to Get Started with the Model

To use this model checkpoint, you need to use the model classes on our GitHub repository.

from segue.modeling_segue import SegueModel
import soundfile

# assuming this is 16kHz mono audio
raw_audio_array, sampling_rate = soundfile.read('example.wav')

model = SegueModel.from_pretrained('declare-lab/segue-w2v2-base')
inputs = model.processor(audio = raw_audio_array, sampling_rate = sampling_rate)
outputs = model(**inputs)

You do not need to create the Processor yourself, it is already available as model.processor.

SegueForRegression and SegueForClassification are also available. For classification, the number of classes can be specified through the n_classes field in model config, e.g. SegueForClassification.from_pretrained('declare-lab/segue-w2v2-base', n_classes=7). Multi-label classification is also supported, e.g. n_classes=[3, 7] for two labels with 3 and 7 classes respectively.

Pre-training and downstream task training scripts are available on our GitHub repository.

Results

We show only simplified MInDS-14 and MELD results for brevity. Please refer to the paper for full results.

MInDS-14 (intent classification)

Note: we used only the en-US subset of MInDS-14.

Fine-tuning

Model	Accuracy
w2v 2.0	89.4±2.3
SEGUE	97.6±0.5

Note: Wav2Vec 2.0 fine-tuning was unstable. Only 3 out of 6 runs converged, the result shown were taken from converged runs only.

Frozen encoder

Model	Accuracy
w2v 2.0	54.0
SEGUE	77.9

MELD (sentiment and emotion classification)

Fine-tuning

Model	Sentiment F1	Emotion F1
w2v 2.0	47.3	39.3
SEGUE	53.2	41.1
SEGUE (higher LR)	54.1	47.2

Note: Wav2Vec 2.0 fine-tuning was unstable at the higher LR.

Frozen encoder

Model	Sentiment F1	Emotion F1
w2v 2.0	45.0±0.7	34.3±1.2
SEGUE	45.8±0.1	35.7±0.3

Limitations

In the paper, we hypothesized that SEGUE may perform worse on tasks that rely less on understanding and more on word detection. This may explain why SEGUE did not manage to improve upon Wav2Vec 2.0 on the Fluent Speech Commands (FSC) task. We also experimented with an ASR task (FLEURS), which heavily relies on word detection, to further demonstrate this.

However, this is does not mean that SEGUE performs worse on intent classification tasks in general. MInDS-14, was able to benifit greatly from SEGUE despite also being an intent classification task, as it has more free-form utterances that may benefit more from understanding.

Citation

@inproceedings{segue2023,
  title={Sentence Embedder Guided Utterance Encoder (SEGUE) for Spoken Language Understanding},
  author={Tan, Yi Xuan and Majumder, Navonil and Poria, Soujanya},
  booktitle={Interspeech},
  year={2023}
}