Fast Speech in Unit Selection Speech Synthesis

Moers-Prinz D. Fast Speech in Unit Selection Speech Synthesis. Bielefeld: Universität Bielefeld; 2020.Speech synthesis is part of the everyday life of many people with severe visual disabilities. For those who are reliant on assistive speech technology the possibility to choose a fast speaking rate is reported to be essential. But also expressive speech synthesis and other spoken language interfaces may require an integration of fast speech. Architectures like formant or diphone synthesis are able to produce synthetic speech at fast speech rates, but the generated speech does not sound very natural. Unit selection synthesis systems, however, are capable of delivering more natural output. Nevertheless, fast speech has not been adequately implemented into such systems to date. Thus, the goal of the work presented here was to determine an optimal strategy for modeling fast speech in unit selection speech synthesis to provide potential users with a more natural sounding alternative for fast speech output

DeepVoCoder: A CNN model for compression and coding of narrow band speech

This paper proposes a convolutional neural network (CNN)-based encoder model to compress and code speech signal directly from raw input speech. Although the model can synthesize wideband speech by implicit bandwidth extension, narrowband is preferred for IP telephony and telecommunications purposes. The model takes time domain speech samples as inputs and encodes them using a cascade of convolutional filters in multiple layers, where pooling is applied after some layers to downsample the encoded speech by half. The final bottleneck layer of the CNN encoder provides an abstract and compact representation of the speech signal. In this paper, it is demonstrated that this compact representation is sufficient to reconstruct the original speech signal in high quality using the CNN decoder. This paper also discusses the theoretical background of why and how CNN may be used for end-to-end speech compression and coding. The complexity, delay, memory requirements, and bit rate versus quality are discussed in the experimental results.Web of Science7750897508

A Parametric Sound Object Model for Sound Texture Synthesis

This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are considered. Four essential processing steps for sound texture analysis are identifi ed, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fi xed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of parameters over time. In contrast to standard spectral modeling techniques, this representation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of diff erent length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of di fferent sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modulation, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations. In contrast to many other forms of sound encoding, the parametric model facilitates various techniques of machine learning and intelligent processing, including sound clustering and principal component analysis. Strengths and weaknesses of the proposed method are reviewed, and possibilities for future development are discussed

Vector adaptive predictive coder for speech and audio

A real-time vector adaptive predictive coder which approximates each vector of K speech samples by using each of M fixed vectors in a first codebook to excite a time-varying synthesis filter and picking the vector that minimizes distortion. Predictive analysis for each frame determines parameters used for computing from vectors in the first codebook zero-state response vectors that are stored at the same address (index) in a second codebook. Encoding of input speech vectors s.sub.n is then carried out using the second codebook. When the vector that minimizes distortion is found, its index is transmitted to a decoder which has a codebook identical to the first codebook of the decoder. There the index is used to read out a vector that is used to synthesize an output speech vector s.sub.n. The parameters used in the encoder are quantized, for example by using a table, and the indices are transmitted to the decoder where they are decoded to specify transfer characteristics of filters used in producing the vector s.sub.n from the receiver codebook vector selected by the vector index transmitted

Optimization Techniques for a Physical Model of Human Vocalisation

We present a non-supervised approach to optimize and evaluate the synthesis of non-speech audio effects from a speech production model. We use the Pink Trombone synthesizer as a case study of a simplified production model of the vocal tract to target non-speech human audio signals --yawnings. We selected and optimized the control parameters of the synthesizer to minimize the difference between real and generated audio. We validated the most common optimization techniques reported in the literature and a specifically designed neural network. We evaluated several popular quality metrics as error functions. These include both objective quality metrics and subjective-equivalent metrics. We compared the results in terms of total error and computational demand. Results show that genetic and swarm optimizers outperform least squares algorithms at the cost of executing slower and that specific combinations of optimizers and audio representations offer significantly different results. The proposed methodology could be used in benchmarking other physical models and audio types.Comment: Accepted to DAFx 202