Source-linked AI summary
An Unsupervised Autoregressive Model for Speech Representation Learning
Yu-An Chung, Wei-Ning Hsu, Hao Tang, James Glass
TL;DR
Speech representation learning needs generic features that preserve information across downstream tasks rather than specializing to a fixed task or discarding potentially useful variation. The paper proposes an unsupervised autoregressive model, APC, and reports improved phone-classification and speaker-verification performance, with different layers emphasizing different speech information.
Problem
Speech representations should preserve information for a wide range of potentially unknown tasks, whereas task-trained transformations can discard information useful for unseen tasks.
Method
The paper proposes Autoregressive Predictive Coding (APC), an unsupervised deep-LSTM model trained autoregressively to predict future speech-spectrum frames.
Results
APC representations significantly outperform CPC models for accessible phonetic content and outperform CPC and i-vector baselines for speaker verification when #(steps) is smaller than 10.
Takeaways & Limitations
APC representations contain both phonetic and speaker information, while different layers capture different levels of speech information.
Takeaways & Limitations
The study uses outputs from a specific APC layer for each downstream task and leaves combining information across layers for future work.
Abstract
from arXiv · showhide
This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that aim to remove noise or speaker variabilities, ours is designed to preserve information for a wide range of downstream tasks. In addition, the proposed model does not require any phonetic or word boundary labels, allowing the model to benefit from large quantities of unlabeled data. Speech representations learned by our model significantly improve performance on both phone classification and speaker verification over the surface features and other supervised and unsupervised approaches. Further analysis shows that different levels of speech information are captured by our model at different layers. In particular, the lower layers tend to be more discriminative for speakers, while the upper layers provide more phonetic content.
1. Introduction
Speech representation learning seeks transformations that make high-level speech properties accessible to downstream tasks without discarding information needed for unknown tasks. This work therefore targets transferable, unsupervised representations that retain broad information from speech signals.
- Speech contains acoustic, linguistic, speaker, intentional, and mental-status information that surface features capture poorly.
- The proposed autoregressive model is unsupervised, transfers across tasks and datasets, and can leverage large quantities of unlabeled data.
- Supervised representations specialized for known tasks can discard information useful for unseen tasks, including noise or speaker variability depending on the task.
- Autoregressive losses are attractive for general representations because they retain signal information without requiring denoising techniques to avoid autoencoding’s trivial identity mapping.
- Prior unsupervised speech studies had not demonstrated transferability across different datasets, motivating general speech representations modeled after large-scale pretrained language-model transfer.
2. Models
The paper introduces Autoregressive Predictive Coding (APC), an unsupervised speech model that predicts future acoustic frames. APC adapts language-modeling ideas to speech while differing from CPC in what its training objective encourages representations to capture.
- APC is a novel autoregressive architecture for unsupervised speech representation learning that predicts the spectrum of a future frame.
- APC adapts language-modeling ideas by using an RNN to model temporal information in an acoustic sequence.Speech frames are fed directly into the RNN rather than mapped through a token lookup table.
- Because speech lacks a finite target-token set, APC replaces the language-model softmax with a regression layer.
- APC predicts a frame n steps ahead to encourage inference of more global speech structure rather than relying only on local signal smoothness.
- CPC separates future target frames from negative samples, whereas APC encodes information sufficient to predict the target frame.The objectives therefore enforce different kinds of information in their learned representations.
3. Experiments
Experiments evaluate APC representations on phone classification and speaker verification, including comparisons with CPC, surface-feature baselines, and i-vectors. Results show stronger phonetic accessibility for APCs, while speaker information is more accessible in lower APC layers.
- Phone classification and speaker verification assess how much phonetic and speaker information APC representations capture.Phone classification uses frame error rates, while speaker verification uses equal error rates.
- Phone Classification: CPC-n9same outperforms CPC-n9all across tested prediction horizons, while context-encoder and exhaustive-negative variants provide richer phonetic content.Same-utterance negatives preserve speaker identity across samples, encouraging CPC-n9same to learn phone information.
- Phone Classification: APCs significantly outperform all CPC models on phone classification despite using a simpler architecture and training approach.The comparison measures phone error rates with linear classifiers.
- Phone Classification: Deeper APC models generally produce better representations, especially for small prediction horizons, with a sweet spot as the target distance varies.The experiments vary both APC depth and the number of future steps predicted.
- Speaker Verification: APCs outperform CPC when predicting fewer than 10 steps ahead and outperform the i-vector baseline in speaker verification.These results indicate that APC representations contain both phonetic and speaker information.
- Speaker Verification: Lower APC layers consistently outperform higher layers for speaker verification, whereas increasing depth improves phone classification.Within a three-layer APC, the first layer outperforms the second, which outperforms the final layer.
4. Discussions
APC learns unsupervised speech representations by autoregressively predicting future frames, with the prediction horizon shaping what information the representation captures. Its representations show useful phone and speaker information, while different layers emphasize different speech properties.
- APC uses a deep LSTM trained autoregressively to predict future speech frames without supervision.A time-shifting factor makes the model predict farther-ahead frames, encouraging discovery of more general speech structures.
- The number of prediction steps controls what APC learns and acts as a prior determined by the representation’s intended use.
- APC extracts useful phone and speaker information, supporting its role as an initial step toward transferable speech representation learning.The paper identifies transfer learning from large-scale pretrained language models as the broader motivation.
- Different APC layers capture different aspects of speech, with lower layers containing richer speaker information than upper layers.The paper suggests combining internal representations across layers as a potential future improvement.