Automatic Accentedness Evaluation of Non-Native Speech Using Phonetic and Sub-Phonetic Posterior Probabilities
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
In this thesis, methods and models are developed and presented aiming at the estimation, restoration and transformation of the characteristics of human speech. During a first period of the thesis, a concept was developed that allows restoring prosodic voic ...
The advent of statistical parametric speech synthesis has paved new ways to a unified framework for hidden Markov model (HMM) based text to speech synthesis (TTS) and automatic speech recognition (ASR). The techniques and advancements made in the field of ...
One of the key challenges involved in building statistical automatic speech recognition (ASR) systems is modeling the relationship between subword units or “lexical units” and acoustic feature observations. To model this relationship two types of resources ...
This paper describes speaker discrimination experiments in which native English listeners were presented with natural speech stimuli in English and Mandarin, synthetic speech stimuli in English and Mandarin, or natural Mandarin speech and synthetic English ...
This paper describes speaker discrimination experiments in which native English listeners were presented with natural speech stimuli in English and Mandarin, synthetic speech stimuli in English and Mandarin, or natural Mandarin speech and synthetic English ...
Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic model- or feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully ...
We propose a stochastic phoneme space transformation technique that allows the conversion of conditional source phoneme posterior probabilities (conditioned on the acoustics) into target phoneme posterior probabilities. The source and target phonemes can b ...
The advent of statistical parametric speech synthesis has paved new ways to a unified framework for hidden Markov model (HMM) based text to speech synthesis (TTS) and automatic speech recognition (ASR). The techniques and advancements made in the field of ...
Ecole Polytechnique Federale de Lausanne (EPFL)2012
Many works on speech processing have dealt with auto-regressive (AR) models for spectral envelope and formant frequency estimation, mostly focusing on the estimation of the AR parameters. However, it is also interesting to be able to directly estimate the ...
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011
Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic model- or feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully ...
