Sparse Autoencoders for Speech Modeling and Recognition
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Speaker diarization is the task of identifying “who spoke when” in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization syste ...
Speaker diarization is the task of identifying ``who spoke when'' in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization sys ...
Phonological features extracted by neural network have shown interesting potential for low bit rate speech vocoding. The span of phonological features is wider than the span of phonetic features, and thus fewer frames need to be transmitted. Moreover, the ...
In this paper, we propose a platform based on phonological speech vocoding for examining relations between phonology and speech processing, and in broader terms, between the abstract and physical structures of speech signal. The goal of this paper is to go ...
Automatic evaluation of non-native speech accentedness has potential implications for not only language learning and accent identification systems but also for speaker and speech recognition systems. From the perspective of speech production, the two prima ...
This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significa ...
Assessment of speech intelligibility is important for the development of speech systems, such as telephony systems and text-to-speech (TTS) systems. Existing approaches to the automatic assessment of intelligibility in telephony typically compare a referen ...
Posterior features have been shown to yield very good performance in multiple contexts including speech recognition, spoken term detection, and template matching. These days, posterior features are usually estimated at the output of a neural network. More ...
Automatic evaluation of non-native speech accentedness has potential implications for not only language learning and accent identification systems but also for speaker and speech recognition systems. From the perspective of speech production, the two prima ...
This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significa ...
