Achieving Multi-Accent ASR via Unsupervised Acoustic Model Adaptation

International audienceCurrent automatic speech recognition (ASR) systems trained on native speech often perform poorly when applied to non-native or accented speech. In this work, we propose to compute x-vector-like accent embeddings and use them as auxiliary inputs to an acoustic model trained on native data only in order to improve the recognition of multi-accent data comprising native, non-native, and accented speech. In addition, we leverage untranscribed accented training data by means of semi-supervised learning. Our experiments show that acoustic models trained with the proposed accent embeddings outperform those trained with conventional i-vector or x-vector speaker embeddings, and achieve a 15% relative word error rate (WER) reduction on non-native and accented speech w.r.t. acoustic models trained with regular spectral features only. Semi-supervised training using just 1 hour of untranscribed speech per accent yields an additional 15% relative WER reduction w.r.t. models trained on native data only

Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation. The overview characterizes adaptation algorithms as based on embeddings, model parameter adaptation, or data augmentation. We present a meta-analysis of the performance of speech recognition adaptation algorithms, based on relative error rate reductions as reported in the literature.Comment: Submitted to IEEE Open Journal of Signal Processing. 30 pages, 27 figure

Learning speech embeddings for speaker adaptation and speech understanding

In recent years, deep neural network models gained popularity as a modeling approach for many speech processing tasks including automatic speech recognition (ASR) and spoken language understanding (SLU). In this dissertation, there are two main goals. The first goal is to propose modeling approaches in order to learn speaker embeddings for speaker adaptation or to learn semantic speech embeddings. The second goal is to introduce training objectives that achieve fairness for the ASR and SLU problems. In the case of speaker adaptation, we introduce an auxiliary network to an ASR model and learn to simultaneously detect speaker changes and adapt to the speaker in an unsupervised way. We show that this joint model leads to lower error rates as compared to a two-step approach where the signal is segmented into single speaker regions and then fed into an adaptation model. We then reformulate the speaker adaptation problem from a counterfactual fairness point-of-view and introduce objective functions to match the ASR performance of the individuals in the dataset to that of their counterfactual counterparts. We show that we can achieve lower error rate in an ASR system while reducing the performance disparity between protected groups. In the second half of the dissertation, we focus on SLU and tackle two problems associated with SLU datasets. The first SLU problem is the lack of large speech corpora. To handle this issue, we propose to use available non-parallel text data so that we can leverage the information in text to guide learning of the speech embeddings. We show that this technique increases the intent classification accuracy as compared to a speech-only system. The second SLU problem is the label imbalance problem in the datasets, which is also related to fairness since a model trained on skewed data usually leads to biased results. To achieve fair SLU, we propose to maximize the F-measure instead of conventional cross-entropy minimization and show that it is possible to increase the number of classes with nonzero recall. In the last two chapters, we provide additional discussions on the impact of these projects from both technical and social perspectives, propose directions for future research and summarize the findings

Enhancing the Generalization of Convolutional Neural Networks for Speech Emotion Recognition

Human-machine interaction is rapidly gaining significance in our daily lives. While speech recognition has achieved near-human performance in recent years, the intricate details embedded in speech extend beyond the mere arrangement of words. Speech Emotion Recognition (SER) is therefore acquiring a growing role in this field by decoding not only the linguistic content but also the emotional nuances of human spoken communication and enabling therefore a more exhaustive comprehension of the information conveyed by speech signals. Despite the success that neural networks have already achieved in this task, SER is still challenging due to the variability of emotional expression, especially in real-world scenarios where generalization to unseen speakers and contexts is required. In addition, the high resource demand of SER models, combined with the scarcity of emotion-labelled data, hinder the development and application of effective solutions in this field. In this thesis, we present multiple approaches to overcome the aforementioned difficulties. We first introduce a multiple-time-scale (MTS) convolutional neural network architecture to create flexibility towards temporal variations when analyzing time-frequency representations of audio data. We show that resilience to speed fluctuations is relevant in SER tasks, since emotion is expressed through complex spectral patterns that can exhibit significant local dilation and compression on the time axis depending on speaker and context. The results indicate that the use of MTS consistently improves the generalization of networks of different capacity and depth, compared to standard convolution. In a second stage, we propose a more general approach to discourage unwanted sensitivity towards specific target properties in CNNs, introducing the novel concept of anti-transfer learning. While transfer learning assumes that the learning process for a target task will benefit from re-using representations learned for another task, anti-transfer avoids the learning of representations that have been learned for an orthogonal task, i.e., one that is not relevant and potentially confounding for the target task, such as speaker identity and speech content for emotion recognition. In anti-transfer learning we penalize similarity between activations of a network being trained and another network previously trained on an orthogonal task. This leads to better generalization and provides a degree of control over correlations that are spurious or undesirable. We show that anti-transfer actually leads to the intended invariance to the orthogonal task and to more appropriate feature maps for the target task at hand. Anti-transfer creates a computation and memory cost at training time, but it enables enables the reuse of pre-trained models. In order to avoid the high resource demand of SER models in general and anti-transfer learning specifically, we propose RH-emo, a novel semisupervised architecture aimed at extracting quaternion embeddings from realvalued monoaural spectrograms, enabling the use of quaternion-valued networks for SER tasks. RH-emo is a hybrid real/quaternion autoencoder network that consists of a real-valued encoder in parallel to a real-valued emotion classifier and a quaternion-valued decoder. We show that the use of RHemo, combined with quaternion convolutional neural networks provides a consistent improvement in SER tasks, while requiring far fewer trainable parameters and therefore substantially reducing the resource demand of SER models. Finally, we apply anti-transfer learning to quaternion-valued neural networks fed with RH-emo embeddings. We demonstrate that the combination of the two approaches maintains the disentanglement properties of antitransfer, while using a reduced amount of memory, computation, and training time, making this a suitable approach for SER scenarios with limited resources and where context and speaker independence are needed