Determination of Formant Features in Czech and Slovak for GMM Emotional Speech Classifier

The paper is aimed at determination of formant features (FF) which describe vocal tract characteristics. It comprises analysis of the first three formant positions together with their bandwidths and the formant tilts. Subsequently, the statistical evaluation and comparison of the FF was performed. This experiment was realized with the speech material in the form of sentences of male and female speakers expressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. The statistical distribution of the analyzed formant frequencies and formant tilts shows good differentiation between neutral and emotional styles for both voices. Contrary to it, the values of the formant 3-dB bandwidths have no correlation with the type of the speaking style or the type of the voice. These spectral parameters together with the values of the other speech characteristics were used in the feature vector for Gaussian mixture models (GMM) emotional speech style classifier that is currently developed. The overall mean classification error rate achieves about 18 %, and the best obtained error rate is 5 % for the sadness style of the female voice. These values are acceptable in this first stage of development of the GMM classifier that should be used for evaluation of the synthetic speech quality after applied voice conversion and emotional speech style transformation

Articulatory-WaveNet: Deep Autoregressive Model for Acoustic-to-Articulatory Inversion

Acoustic-to-Articulatory Inversion, the estimation of articulatory kinematics from speech, is an important problem which has received significant attention in recent years. Estimated articulatory movements from such models can be used for many applications, including speech synthesis, automatic speech recognition, and facial kinematics for talking-head animation devices. Knowledge about the position of the articulators can also be extremely useful in speech therapy systems and Computer-Aided Language Learning (CALL) and Computer-Aided Pronunciation Training (CAPT) systems for second language learners. Acoustic-to-Articulatory Inversion is a challenging problem due to the complexity of articulation patterns and significant inter-speaker differences. This is even more challenging when applied to non-native speakers without any kinematic training data. This dissertation attempts to address these problems through the development of up-graded architectures for Articulatory Inversion. The proposed Articulatory-WaveNet architecture is based on a dilated causal convolutional layer structure that improves the Acoustic-to-Articulatory Inversion estimated results for both speaker-dependent and speaker-independent scenarios. The system has been evaluated on the ElectroMagnetic Articulography corpus of Mandarin Accented English (EMA-MAE) corpus, consisting of 39 speakers including both native English speakers and Mandarin accented English speakers. Results show that Articulatory-WaveNet improves the performance of the speaker-dependent and speaker-independent Acoustic-to-Articulatory Inversion systems significantly compared to the previously reported results

Improving the Speech Intelligibility By Cochlear Implant Users

In this thesis, we focus on improving the intelligibility of speech for cochlear implants (CI) users. As an auditory prosthetic device, CI can restore hearing sensations for most patients with profound hearing loss in both ears in a quiet background. However, CI users still have serious problems in understanding speech in noisy and reverberant environments. Also, bandwidth limitation, missing temporal fine structures, and reduced spectral resolution due to a limited number of electrodes are other factors that raise the difficulty of hearing in noisy conditions for CI users, regardless of the type of noise. To mitigate these difficulties for CI listener, we investigate several contributing factors such as the effects of low harmonics on tone identification in natural and vocoded speech, the contribution of matched envelope dynamic range to the binaural benefits and contribution of low-frequency harmonics to tone identification in quiet and six-talker babble background. These results revealed several promising methods for improving speech intelligibility for CI patients. In addition, we investigate the benefits of voice conversion in improving speech intelligibility for CI users, which was motivated by an earlier study showing that familiarity with a talker’s voice can improve understanding of the conversation. Research has shown that when adults are familiar with someone’s voice, they can more accurately – and even more quickly – process and understand what the person is saying. This theory identified as the “familiar talker advantage” was our motivation to examine its effect on CI patients using voice conversion technique. In the present research, we propose a new method based on multi-channel voice conversion to improve the intelligibility of transformed speeches for CI patients

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