Unsupervised Acoustic Unit Representation Learning for Voice Conversion using WaveNet Auto-encoders

Unsupervised representation learning of speech has been of keen interest in recent years, which is for example evident in the wide interest of the ZeroSpeech challenges. This work presents a new method for learning frame level representations based on WaveNet auto-encoders. Of particular interest in the ZeroSpeech Challenge 2019 were models with discrete latent variable such as the Vector Quantized Variational Auto-Encoder (VQVAE). However these models generate speech with relatively poor quality. In this work we aim to address this with two approaches: first WaveNet is used as the decoder and to generate waveform data directly from the latent representation; second, the low complexity of latent representations is improved with two alternative disentanglement learning methods, namely instance normalization and sliced vector quantization. The method was developed and tested in the context of the recent ZeroSpeech challenge 2020. The system output submitted to the challenge obtained the top position for naturalness (Mean Opinion Score 4.06), top position for intelligibility (Character Error Rate 0.15), and third position for the quality of the representation (ABX test score 12.5). These and further analysis in this paper illustrates that quality of the converted speech and the acoustic units representation can be well balanced.Comment: To be presented in Interspeech 202

Multilingual and Unsupervised Subword Modelingfor Zero-Resource Languages

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should capture phonetic content and abstract away from other types of variability, such as speaker differences and channel noise. Previous work in this area has primarily focused unsupervised learning from target language data only, and has been evaluated only intrinsically. Here we directly compare multiple methods, including some that use only target language speech data and some that use transcribed speech from other (non-target) languages, and we evaluate using two intrinsic measures as well as on a downstream unsupervised word segmentation and clustering task. We find that combining two existing target-language-only methods yields better features than either method alone. Nevertheless, even better results are obtained by extracting target language bottleneck features using a model trained on other languages. Cross-lingual training using just one other language is enough to provide this benefit, but multilingual training helps even more. In addition to these results, which hold across both intrinsic measures and the extrinsic task, we discuss the qualitative differences between the different types of learned features.Comment: 17 pages, 6 figures, 7 tables. Accepted for publication in Computer Speech and Language. arXiv admin note: text overlap with arXiv:1803.0886

Unsupervised acoustic unit representation learning for voice conversion using WaveNet auto-encoders

Unsupervised representation learning of speech has been of keen interest in recent years, which is for example evident in the wide interest of the ZeroSpeech challenges. This work presents a new method for learning frame level representations based on WaveNet auto-encoders. Of particular interest in the ZeroSpeech Challenge 2019 were models with discrete latent variable such as the Vector Quantized Variational Auto-Encoder (VQVAE). However these models generate speech with relatively poor quality. In this work we aim to address this with two approaches: first WaveNet is used as the decoder and to generate waveform data directly from the latent representation; second, the low complexity of latent representations is improved with two alternative disentanglement learning methods, namely instance normalization and sliced vector quantization. The method was developed and tested in the context of the recent ZeroSpeech challenge 2020. The system output submitted to the challenge obtained the top position for naturalness (Mean Opinion Score 4.06), top position for intelligibility (Character Error Rate 0.15), and third position for the quality of the representation (ABX test score 12.5). These and further analysis in this paper illustrates that quality of the converted speech and the acoustic units representation can be well balanced

Disentanglement Learning for Text-Free Voice Conversion

Voice conversion (VC) aims to change the perceived speaker identity of a speech signal from one to another, while preserving the linguistic content. Recent state-of-the-art VC systems typically are dependent on automatic speech recognition (ASR) models and they have gained great successes. Results of recent challenges show these VC systems have reached a level of performance close to real human voices. However, they are highly relying on the performance of the ASR models, which might experience degradations in practical applications because of the mismatch between training and test data. VC systems independent of ASR models are typically regarded as text-free systems. They commonly apply disentanglement learning methods to remove the speaker information of a speech signal, for example, vector quantisation (VQ) or instance normalisation (IN). However, text-free VC systems have not reached the same level of performance as text-dependent systems. This thesis mainly studies disentanglement learning methods for improving the performance of text-free VC systems. Three major contributions are summarised as follows. Firstly, in order to improve the performance of an auto-encoder based VC model, the information loss issue caused by the VQ of the model is studied. Two disentanglement learning methods are exploited to replace the VQ of the model. Experiments show that these two methods improve the naturalness and intelligibility performance of the model, but hurt the speaker similarity performance of the model. The reason for the degradation of the speaker similarity performance is studied in the further analysis experiments. Next, the performance and the robustness of Generative Adversarial Networks (GAN) based VC models are studied. In order to improve the performance and the robustness of an GAN based VC model, a new model is proposed. This new model introduces a new speaker adaptation layer for alleviating the information loss issue caused by a speaker adaptation method based on IN. Experiments show that the proposed model outperformed the baseline models on VC performance and robustness. The third contribution studies whether Self-Supervised Learning (SSL) based VC models can reach the same level of performance of the state-of-the-art text-dependent models. An encoder-decoder framework is established for experiments. In this framework, the performance of a VC systems implemented with a SSL model can be compared to a VC system implemented with an ASR model. Experiment results show that SSL based VC models can reach the same level of naturalness performance of the state-of-the-art text- dependent VC models. Also, SSL based VC models gained advantages on intelligibility performance when tested on out of domain target speakers. But they performed worse on speaker similarity

Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications

The representation learning of speech, without textual resources, is an area of significant interest for many low resource speech applications. In this paper, we describe an approach to self-supervised representation learning from raw audio using a hidden unit clustering (HUC) framework. The input to the model consists of audio samples that are windowed and processed with 1-D convolutional layers. The learned "time-frequency" representations from the convolutional neural network (CNN) module are further processed with long short term memory (LSTM) layers which generate a contextual vector representation for every windowed segment. The HUC framework, allowing the categorization of the representations into a small number of phoneme-like units, is used to train the model for learning semantically rich speech representations. The targets consist of phoneme-like pseudo labels for each audio segment and these are generated with an iterative k-means algorithm. We explore techniques that improve the speaker invariance of the learned representations and illustrate the effectiveness of the proposed approach on two settings, i) completely unsupervised speech applications on the sub-tasks described as part of the ZeroSpeech 2021 challenge and ii) semi-supervised automatic speech recognition (ASR) applications on the TIMIT dataset and on the GramVaani challenge Hindi dataset. In these experiments, we achieve state-of-art results for various ZeroSpeech tasks. Further, on the ASR experiments, the HUC representations are shown to improve significantly over other established benchmarks based on Wav2vec, HuBERT and Best-RQ