Privacy-Sensitive Audio Features for Conversational Speech Processing
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In this paper, we investigate employment of discriminatively trained acoustic features modeled by Subspace Gaussian Mixture Models (SGMMs) for Rich Transcription meeting recognition. More specifically, first, we focus on exploiting various types of complex ...
Standard automatic speech recognition (ASR) systems use phonemes as subword units. Thus, one of the primary resource required to build a good ASR system is a well developed phoneme pronunciation lexicon. However, under-resourced languages typically lack su ...
This paper investigates robust privacy-sensitive audio features for speaker diarization in multiparty conversations: ie., a set of audio features having low linguistic information for speaker diarization in a single and multiple distant microphone scenario ...
Vocal tract length normalisation (VTLN) is a well known rapid adaptation technique. VTLN as a linear transformation in the cepstral domain results in the scaling and translation factors. The warping factor represents the spectral scaling parameter. While, ...
The Montreux Jazz Festival archives covers almost a half-century of live jazz, rock and pop music, gathering international first-class musicians within a single catalogue. In the frame of the digitization and valorisation of the whole archives by EPFL rese ...
Setting out from the point of view that automatic speech recognition (ASR) ought to benefit from data in languages other than the target language, we propose a novel Kullback-Leibler (KL) divergence based method that is able to exploit multilingual informa ...
This work aims at investigating the automatic recognition of speaker role in meeting conversations from the AMI corpus. Two types of roles are considered: formal roles, fixed over the meeting duration and recognized at recording level, and social roles rel ...
Recurrent neural network language models (RNNLMs) have recently shown to outperform the venerable n-gram language models (LMs). However, in automatic speech recognition (ASR), RNNLMs were not yet used to directly decode a speech signal. Instead, RNNLMs are ...
Recurrent neural network language models (RNNLMs) have recently shown to outperform the venerable n-gram language models (LMs). However, in automatic speech recognition (ASR), RNNLMs were not yet used to directly decode a speech signal. Instead, RNNLMs are ...
Standard automatic speech recognition (ASR) systems use phonemes as subword units. Thus, one of the primary resource required to build a good ASR system is a well developed phoneme pronunciation lexicon. However, under-resourced languages typically lack su ...
