Using contextual information in Joint Factor Eigenspace MLLR for speech recognition in diverse scenarios

This paper presents a new approach for rapid adaptation in the presence of highly diverse scenarios that takes advantage of information describing the input signals. We introduce a new method for joint factorisation of the background and the speaker in an eigenspace MLLR framework: Joint Factor Eigenspace MLLR (JFEMLLR). We further propose to use contextual information describing the speaker and background, such as tags or more complex metadata, to provide an immediate estimation of the best MLLR transformation for the utterance. This provides instant adaptation, since it does not require any transcription from a previous decoding stage. Evaluation in a highly diverse Automatic Speech Recognition (ASR) task, a modified version of WSJCAM0, yields an improvement of 26.9% over the baseline, which is an extra 1.2% reduction over two-pass MLLR adaptation

Acoustic Adaptation to Dynamic Background Conditions with Asynchronous Transformations

This paper proposes a framework for performing adaptation to complex and non-stationary background conditions in Automatic Speech Recognition (ASR) by means of asynchronous Constrained Maximum Likelihood Linear Regression (aCMLLR) transforms and asynchronous Noise Adaptive Training (aNAT). The proposed method aims to apply the feature transform that best compensates the background for every input frame. The implementation is done with a new Hidden Markov Model (HMM) topology that expands the usual left-to-right HMM into parallel branches adapted to different background conditions and permits transitions among them. Using this, the proposed adaptation does not require ground truth or previous knowledge about the background in each frame as it aims to maximise the overall log-likelihood of the decoded utterance. The proposed aCMLLR transforms can be further improved by retraining models in an aNAT fashion and by using speaker-based MLLR transforms in cascade for an efficient modelling of background effects and speaker. An initial evaluation in a modified version of the WSJCAM0 corpus incorporating 7 different background conditions provides a benchmark in which to evaluate the use of aCMLLR transforms. A relative reduction of 40.5% in Word Error Rate (WER) was achieved by the combined use of aCMLLR and MLLR in cascade. Finally, this selection of techniques was applied in the transcription of multi-genre media broadcasts, where the use of aNAT training, aCMLLR transforms and MLLR transforms provided a relative improvement of 2–3%

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