A Log Domain Pulse Model for Parametric Speech Synthesis

Most of the degradation in current Statistical Parametric Speech Synthesis (SPSS) results from the form of the vocoder. One of the main causes of degradation is the reconstruction of the noise. In this article, a new signal model is proposed that leads to a simple synthesizer, without the need for ad-hoc tuning of model parameters. The model is not based on the traditional additive linear source-filter model, it adopts a combination of speech components that are additive in the log domain. Also, the same representation for voiced and unvoiced segments is used, rather than relying on binary voicing decisions. This avoids voicing error discontinuities that can occur in many current vocoders. A simple binary mask is used to denote the presence of noise in the time-frequency domain, which is less sensitive to classification errors. Four experiments have been carried out to evaluate this new model. The first experiment examines the noise reconstruction issue. Three listening tests have also been carried out that demonstrate the advantages of this model: comparison with the STRAIGHT vocoder; the direct prediction of the binary noise mask by using a mixed output configuration; and partial improvements of creakiness using a mask correction mechanism.European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie; 10.13039/501100000266-EPSR

Studies on noise robust automatic speech recognition

Noise in everyday acoustic environments such as cars, traffic environments, and cafeterias remains one of the main challenges in automatic speech recognition (ASR). As a research theme, it has received wide attention in conferences and scientific journals focused on speech technology. This article collection reviews both the classic and novel approaches suggested for noise robust ASR. The articles are literature reviews written for the spring 2009 seminar course on noise robust automatic speech recognition (course code T-61.6060) held at TKK

Hidden Markov Models for Visual Speech Synthesis in Limited Data

This work presents a new approach for estimating control points (facial locations that control movement) to allow the artificial generation of video with apparent mouth movement (visual speech) time-synced with recorded audio. First, Hidden Markov Models (HMMs) are estimated for each visual speech category (viseme) present in stored video data, where a category is defined as the mouth movement corresponding to a given sound and where the visemes are further categorized as trisemes (a viseme in the context of previous and following visemes). Next, a decision tree is used to cluster and relate states in the HMMs that are similar in a contextual and statistical sense. The tree is also used to estimate HMMs that generate sequences of visual speech control points for trisemes not occurring in the stored data. An experiment is described that evaluates the effect of several algorithm variables, and a statistical analysis is presented that establishes appropriate levels for each variable by minimizing the error between the desired and estimated control points. The analysis indicates that the error is lowest when the process is conducted with three-state left-to right no skip HMMs trained using short-duration dynamic features, a high log-likelihood threshold, and a low outlier threshold. Also, comparisons of mouth shapes generated from the artificial control points and the true control points (estimated from video not used to train the HMMs) indicate that the process provides accurate estimates for most trisemes tested in this work. The research presented here thus establishes a useful method for synthesizing realistic audio-synchronized video facial features