Cross-lingual studies of ASR errors: paradigms for perceptual evaluations
نویسندگان
چکیده
It is well-known that human listeners significantly outperform machines when it comes to transcribing speech. This paper presents a progress report of the joint research in the automatic vs human speech transcription and of the perceptual experiments developed at LIMSI that aims to increase our understanding of automatic speech recognition errors. Two paradigms are described here in which human listeners are asked to transcribe speech segments containing words that are frequently misrecognized by the system. In particular, we sought to gain information about the impact of increased context to help humans disambiguate problematic lexical items, typically homophone or near-homophone words. The long-term aim of this research is to improve the modeling of ambiguous contexts so as to reduce automatic transcription errors.
منابع مشابه
Cross-Lingual Study of ASR Errors: On the Role of the Context in Human Perception of Near-Homophones
It is widely acknowledged that human listeners significantly outperform machines when it comes to transcribing speech. This paper presents a paradigm for perceptual experiments that aims to increase our understanding of human and automatic speech recognition errors. The role of the context length is investigated through perceptual recovery of small homophonic words or near-homophones yielding f...
متن کاملDesign of Cross-lingual and Multilingual Corpora for Speaker Recognition Research and Evaluation in Indian Languages
Automatic Speaker Recognition (ASR) is an economic method of biometrics because of the availability of the low cost and powerful processors. Results of ASR are highly dependent on database, i.e., the results obtained in an ASR system are meaningless if the recording conditions are not of standard. In this paper, a methodology and a typical experimental setup used for development of corpora for ...
متن کاملAnalysis of Mismatched Transcriptions Generated by Humans and Machines for Under-Resourced Languages
When speech data with native transcriptions are scarce in an under-resourced language, automatic speech recognition (ASR) must be trained using other methods. Semi-supervised learning first labels the speech using ASR from other languages, then re-trains the ASR using the generated labels. Mismatched crowdsourcing asks crowd-workers unfamiliar with the language to transcribe it. In this paper, ...
متن کاملA perceptual investigation of speech transcription errors involving frequent near-homophones in French and american English
This article compares the errors made by automatic speech recognizers to those made by humans for near-homophones in American English and French. This exploratory study focuses on the impact of limited word context and the potential resulting ambiguities for automatic speech recognition (ASR) systems and human listeners. Perceptual experiments using 7-gram chunks centered on incorrect or correc...
متن کاملCross-lingual portability of MLP-based tandem features - a case study for English and Hungarian
One promising approach for building ASR systems for lessresourced languages is cross-lingual adaptation. Tandem ASR is particularly well suited to such adaptation, as it includes two cascaded modelling steps: feature extraction using multi-layer perceptrons (MLPs), followed by modelling using a standard HMM. The language-specific tuning can be performed by adjusting the HMM only, leaving the ML...
متن کامل