17 ways to say yes:Toward nuanced tone of voice in AAC and speech technology

People with complex communication needs who use speech-generating devices have very little expressive control over their tone of voice. Despite its importance in human interaction, the issue of tone of voice remains all but absent from AAC research and development however. In this paper, we describe three interdisciplinary projects, past, present and future: The critical design collection Six Speaking Chairs has provoked deeper discussion and inspired a social model of tone of voice; the speculative concept Speech Hedge illustrates challenges and opportunities in designing more expressive user interfaces; the pilot project Tonetable could enable participatory research and seed a research network around tone of voice. We speculate that more radical interactions might expand frontiers of AAC and disrupt speech technology as a whole

Stress and Accent Transmission In HMM-Based Syllable-Context Very Low Bit Rate Speech Coding

Abstract In this paper, we propose a solution to reconstruct stress and accent contextual factors at the receiver of a very low bitrate speech codec built on recognition/synthesis architecture. In speech synthesis, accent and stress symbols are predicted from the text, which is not available at the receiver side of the speech codec. Therefore, speech signal-based symbols, generated as syllable-level log average F0 and energy acoustic measures, quantized using a scalar quantization, are used instead of accentual and stress symbols for HMM-based speech synthesis. Results from incremental real-time speech synthesis confirmed, that a combination of F0 and energy signal-based symbols can replace their counterparts of text-based binary accent and stress symbols developed for text-to-speech systems. The estimated transmission bit-rate overhead is about 14 bits/second per acoustic measure

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

Reactive Statistical Mapping: Towards the Sketching of Performative Control with Data

Part 1: Fundamental IssuesInternational audienceThis paper presents the results of our participation to the ninth eNTERFACE workshop on multimodal user interfaces. Our target for this workshop was to bring some technologies currently used in speech recognition and synthesis to a new level, i.e. being the core of a new HMM-based mapping system. The idea of statistical mapping has been investigated, more precisely how to use Gaussian Mixture Models and Hidden Markov Models for realtime and reactive generation of new trajectories from inputted labels and for realtime regression in a continuous-to-continuous use case. As a result, we have developed several proofs of concept, including an incremental speech synthesiser, a software for exploring stylistic spaces for gait and facial motion in realtime, a reactive audiovisual laughter and a prototype demonstrating the realtime reconstruction of lower body gait motion strictly from upper body motion, with conservation of the stylistic properties. This project has been the opportunity to formalise HMM-based mapping, integrate various of these innovations into the Mage library and explore the development of a realtime gesture recognition tool

REACTIVE AND CONTINUOUS CONTROL OF HMM-BASED SPEECH SYNTHESIS

In this paper, we present a modified version of HTS, called performative HTS or pHTS. The objective of pHTS is to enhance the control ability and reactivity of HTS. pHTS reduces the phonetic context used for training the models and generates the speech parameters within a 2-label window. Speech waveforms are generated on-the-fly and the models can be reactively modified, impacting the synthesized speech with a delay of only one phoneme. It is shown that HTS and pHTS have comparable output quality. We use this new system to achieve reactive model interpolation and conduct a new test where articulation degree is modified within the sentence. Index Terms — speech synthesis, HTS, reactive control 1