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 [BibTeX] [Marc21]
HMM-based Non-native Accent Assessment using Posterior Features
Type of publication: Conference paper
Citation: Rasipuram_INTERSPEECH_2016
Publication status: Accepted
Booktitle: Proceedings of Interspeech
Year: 2016
Month: September
Location: San Francisco, USA
Crossref: Rasipuram_Idiap-RR-32-2015:
Abstract: Automatic non-native accent assessment has potential benefits in language learning and speech technologies. The three fundamental challenges in automatic accent assessment are to characterize, model and assess individual variation in speech of the non-native speaker. In our recent work, accentedness score was automatically obtained by comparing two phone probability sequences obtained through instances of non-native and native speech. Although automatic accentedness ratings of the approach correlated well with human accent ratings, the approach is critically constrained because of the requirement of native speech instance. In this paper, we build on the previous work and obtain the native latent symbol probability sequence through the word hypothesis modeled as a hidden Markov model (HMM). The latent symbols are either context-independent phonemes or clustered context-dependent phonemes. The advantage of the proposed approach is that it requires just reference text transcription instead of native speech recordings. Using the HMMs trained on an auxiliary native speech corpus, the proposed approach achieves a correlation of 0.68 with human accent ratings on the ISLE corpus. This is further interesting considering that the approach does not use any non-native data and human accent ratings at any stage of the system development.
Keywords: Automatic accent assessment, KL-divergence, lexical model, non-native speech, Posterior features
Projects Idiap
Authors Rasipuram, Ramya
Cernak, Milos
Magimai.-Doss, Mathew
Added by: [UNK]
Total mark: 0
  • Rasipuram_INTERSPEECH2016_2016.pdf