Incremental Syllable-Context Phonetic Vocoding

Current very low bit rate speech coders are, due to complexity limitations, designed to work off-line. This paper investigates incremental speech coding that operates real-time and incrementally (i.e., encoded speech depends only on already-uttered speech without the need of future speech information). Since human speech communication is asynchronous (i.e., different information flows being simultaneously processed), we hypothesised that such an incremental speech coder should also operate asynchronously. To accomplish this task, we describe speech coding that reflects the human cortical temporal sampling that packages information into units of different temporal granularity, such as phonemes and syllables, in parallel. More specifically, a phonetic vocoder — cascaded speech recognition and synthesis systems — extended with syllable-based information transmission mechanisms is investigated. There are two main aspects evaluated in this work, the synchronous and asynchronous coding. Synchronous coding refers to the case when the phonetic vocoder and speech generation process depend on the syllable boundaries during encoding and decoding respectively. On the other hand, asynchronous coding refers to the case when the phonetic encoding and speech generation processes are done independently of the syllable boundaries. Our experiments confirmed that the asynchronous incremental speech coding performs better, in terms of intelligibility and overall speech quality, mainly due to better alignment of the segmental and prosodic information. The proposed vocoding operates at an uncompressed bit rate of 213 bits/sec and achieves an average communication delay of 243 ms

Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding

Most current very low bit rate (VLBR) speech coding systems use hidden Markov model (HMM) based speech recognition/synthesis techniques. This allows transmission of information (such as phonemes) segment by segment that decreases the bit rate. However, the encoder based on a phoneme speech recognition may create bursts of segmental errors. Segmental errors are further propagated to optional suprasegmental (such as syllable) information coding. Together with the errors of voicing detection in pitch parametrization, HMM-based speech coding creates speech discontinuities and unnatural speech sound artefacts. In this paper, we propose a novel VLBR speech coding framework based on neural networks (NNs) for end-to-end speech analysis and synthesis without HMMs. The speech coding framework relies on phonological (sub-phonetic) representation of speech, and it is designed as a composition of deep and spiking NNs: a bank of phonological analysers at the transmitter, and a phonological synthesizer at the receiver, both realised as deep NNs, and a spiking NN as an incremental and robust encoder of syllable boundaries for coding of continuous fundamental frequency (F0). A combination of phonological features defines much more sound patterns than phonetic features defined by HMM-based speech coders, and the finer analysis/synthesis code contributes into smoother encoded speech. Listeners significantly prefer the NN-based approach due to fewer discontinuities and speech artefacts of the encoded speech. A single forward pass is required during the speech encoding and decoding. The proposed VLBR speech coding operates at a bit rate of approximately 360 bits/s

Phonological vocoding using artificial neural networks

We investigate a vocoder based on artificial neural networks using a phonological speech representation. Speech decomposition is based on the phonological encoders, realised as neural network classifiers, that are trained for a particular language. The speech reconstruction process involves using a Deep Neural Network (DNN) to map phonological features posteriors to speech parameters -- line spectra and glottal signal parameters -- followed by LPC resynthesis. This DNN is trained on a target voice without transcriptions, in a semi-supervised manner. Both encoder and decoder are based on neural networks and thus the vocoding is achieved using a simple fast forward pass. An experiment with French vocoding and a target male voice trained on 21 hour long audio book is presented. An application of the phonological vocoder to low bit rate speech coding is shown, where transmitted phonological posteriors are pruned and quantized. The vocoder with scalar quantization operates at 1 kbps, with potential for lower bit-rate

On Compressibility of Neural Network phonological Features for Low Bit Rate Speech Coding

Phonological features extracted by neural network have shown interesting potential for low bit rate speech vocoding. The span of phonological features is wider than the span of phonetic features, and thus fewer frames need to be transmitted. Moreover, the binary nature of phonological features enables a higher compression ratio at minor quality cost. In this paper, we study the compressibility and structured sparsity of the phonological features. We propose a compressive sampling framework for speech coding and sparse reconstruction for decoding prior to synthesis. Compressive sampling is found to be a principled way for compression in contrast to the conventional pruning approach; it leads to $50$\% reduction in the bit-rate for better or equal quality of the decoded speech. Furthermore, exploiting the structured sparsity and binary characteristic of these features have shown to enable very low bit-rate coding at 700 bps with negligible quality loss; this coding scheme imposes no latency. If we consider a latency of $256$~ms for supra-segmental structures, the rate of $250-350$~bps is achieved

Syllabic Pitch Tuning for Neutral-to-Emotional Voice Conversion

Prosody plays an important role in both identification and synthesis of emotionalized speech. Prosodic features like pitch are usually estimated and altered at a segmental level based on short windows of speech (where the signal is expected to be quasi-stationary). This results in a frame-wise change of acoustical parameters for synthesizing emotionalized speech. In order to convert a neutral speech to an emotional speech from the same user, it might be better to alter the pitch parameters at the suprasegmental level like at the syllable-level since the changes in the signal are more subtle and smooth. In this paper we aim to show that the pitch transformation in a neutral-to-emotional voice conversion system may result in a better speech quality output if the transformations are performed at the supra-segmental (syllable) level rather than a frame-level change. Subjective evaluation results are shown to demonstrate if the naturalness, speaker similarity and the emotion recognition tasks show any performance difference

Statistical parametric speech synthesis using conversational data and phenomena

Statistical parametric text-to-speech synthesis currently relies on predefined and highly controlled prompts read in a “neutral” voice. This thesis presents work on utilising recordings of free conversation for the purpose of filled pause synthesis and as an inspiration for improved general modelling of speech for text-to-speech synthesis purposes. A corpus of both standard prompts and free conversation is presented and the potential usefulness of conversational speech as the basis for text-to-speech voices is validated. Additionally, through psycholinguistic experimentation it is shown that filled pauses can have potential subconscious benefits to the listener but that current text-to-speech voices cannot replicate these effects. A method for pronunciation variant forced alignment is presented in order to obtain a more accurate automatic speech segmentation something which is particularly bad for spontaneously produced speech. This pronunciation variant alignment is utilised not only to create a more accurate underlying acoustic model, but also as the driving force behind creating more natural pronunciation prediction at synthesis time. While this improves both the standard and spontaneous voices the naturalness of spontaneous speech based voices still lags behind the quality of voices based on standard read prompts. Thus, the synthesis of filled pauses is investigated in relation to specific phonetic modelling of filled pauses and through techniques for the mixing of standard prompts with spontaneous utterances in order to retain the higher quality of standard speech based voices while still utilising the spontaneous speech for filled pause modelling. A method for predicting where to insert filled pauses in the speech stream is also developed and presented, relying on an analysis of human filled pause usage and a mix of language modelling methods. The method achieves an insertion accuracy in close agreement with human usage. The various approaches are evaluated and their improvements documented throughout the thesis, however, at the end the resulting filled pause quality is assessed through a repetition of the psycholinguistic experiments and an evaluation of the compilation of all developed methods

Prosody generation for text-to-speech synthesis

The absence of convincing intonation makes current parametric speech synthesis systems sound dull and lifeless, even when trained on expressive speech data. Typically, these systems use regression techniques to predict the fundamental frequency (F0) frame-by-frame. This approach leads to overlysmooth pitch contours and fails to construct an appropriate prosodic structure across the full utterance. In order to capture and reproduce larger-scale pitch patterns, we propose a template-based approach for automatic F0 generation, where per-syllable pitch-contour templates (from a small, automatically learned set) are predicted by a recurrent neural network (RNN). The use of syllable templates mitigates the over-smoothing problem and is able to reproduce pitch patterns observed in the data. The use of an RNN, paired with connectionist temporal classification (CTC), enables the prediction of structure in the pitch contour spanning the entire utterance. This novel F0 prediction system is used alongside separate LSTMs for predicting phone durations and the other acoustic features, to construct a complete text-to-speech system. Later, we investigate the benefits of including long-range dependencies in duration prediction at frame-level using uni-directional recurrent neural networks. Since prosody is a supra-segmental property, we consider an alternate approach to intonation generation which exploits long-term dependencies of F0 by effective modelling of linguistic features using recurrent neural networks. For this purpose, we propose a hierarchical encoder-decoder and multi-resolution parallel encoder where the encoder takes word and higher level linguistic features at the input and upsamples them to phone-level through a series of hidden layers and is integrated into a Hybrid system which is then submitted to Blizzard challenge workshop. We then highlight some of the issues in current approaches and a plan for future directions of investigation is outlined along with on-going work