Spoken Language Recognition: From Fundamentals to Practice

Li, Haizhou; Ma, Bin; Lee, Kong Aik

doi:10.1109/jproc.2012.2237151

Cited by 247 publications

(144 citation statements)

References 90 publications

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“…In this study we treat foreign accent recognition as a language recognition task typically accomplished via either acoustic or phonotactic modeling [5]. In the former approach, acoustic features, such as shifted delta cepstra This work was partially supported by Academy of Finland (projects 253000 and 253120).…”

Section: Introductionmentioning

confidence: 99%

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Introducing attribute features to foreign accent recognition

Behravan

Siniscalchi

Kinnunen

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a hybrid approach to foreign accent recognition combining both phonotactic and spectral based systems by treating the problem as a spoken language recognition task. We extract speech attribute features that represent speech and acoustic cues reflecting foreign accents of a speaker to obtain feature streams that are modeled with the i-vector methodology. Testing on the Finnish Language Proficiency exam corpus, we find our proposed technique to achieve a significant performance improvement over the state-of-the-art systems using only spectral based features.

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Section: Introductionmentioning

confidence: 99%

“…However, knowledge based modeling, such as phonotactic features, are known to be linguistically and phonetically relevant [5]. However, the front-end of the phonotactic system needs a tokenizer that will turn the utterance into a sequence of "phonetic letters" [15,16].…”

Section: Introductionmentioning

confidence: 99%

Introducing attribute features to foreign accent recognition

Behravan

Siniscalchi

Kinnunen

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In most cases, communication is limited on the robot side due to limited sensing and perception capabilities, and this is the original motivation of human-robot collaboration. Technologies such as intention estimation [5], speech recognition [9], human detection and tracking [10,11], and gesture recognition [12,13] have been developed to enhance the perception capability of the robot. Augmented reality has been studied to make the human being and robot share the same reference frame [1].…”

Section: Communication and Interfacementioning

confidence: 99%

Building Companionship through Human-Robot Collaboration

Tee

et al. 2013

Social Robotics

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Abstract. While human-robot collaboration has been studied intensively in the literature, little attention has been given to understanding the role of collaborative endeavours on enhancing the companionship between humans and robots. In this position paper, we explore the possibilities of building the human-robot companionship through collaborative activities. The design guideline of a companion robot Nancy developed at SRL is introduced, and preliminary studies on human-robot collaboration conducted at SRL and I 2 R are elaborated. Critical issues and technical challenges in human-robot collaboration systems are discussed. Through these discussions, we aim to draw the attention of the social robotics community to the importance of human-robot collaboration in companionship building, and stimulate more research effort in this emerging area.

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“…multilingual speech processing systems) or human listeners (i.e. call routing to a proper human operator) [7]. Therefore, accurate and efficient behaviour in real-time applications is often essential, for example, when used for emergency call routing, where the response time of a fluent native operator is critical [1] [8].…”

Section: Introductionmentioning

confidence: 99%

“…Driven by recent developments in speaker verification, the current state-of-the-art in acoustic LID systems involves using i-vector front-end features followed by diverse classification mechanisms that compensate speaker and session variabilities [7] [10] [11]. The i-vector is a compact representation (typically from 400 to 600 dimensions) of a whole utterance, derived as a point estimate of the latent variables in a factor analysis model [12] [13].…”

Section: Introductionmentioning

confidence: 99%

Frame-by-frame language identification in short utterances using deep neural networks

et al. 2015

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in:Neural Networks 64 (2015) AbstractThis work addresses the use of deep neural networks (DNNs) in automatic language identification (LID) focused on short test utterances. Motivated by their recent success in acoustic modelling for speech recognition, we adapt DNNs to the problem of identifying the language in a given utterance from the short-term acoustic features. We show how DNNs are particularly suitable to perform LID in real-time applications, due to their capacity to emit a language identification posterior at each new frame of the test utterance. We then analyse different aspects of the system, such as the amount of required training data, the number of hidden layers, the relevance of contextual information and the effect of the test utterance duration. Finally, we propose several methods to combine frame-by-frame posteriors. Experiments are conducted on two different datasets: the public NIST Language Recognition Evaluation 2009 (3 seconds task) and a much larger corpus (of 5 million utterances) known as Google 5M LID, obtained from different Google Services. Reported results show relative improvements of DNNs versus the i-vector system of 40% in LRE09 3 second task and 76% in Google 5M LID.

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Spoken Language Recognition: From Fundamentals to Practice

Cited by 247 publications

References 90 publications

Introducing attribute features to foreign accent recognition

Introducing attribute features to foreign accent recognition

Building Companionship through Human-Robot Collaboration

Frame-by-frame language identification in short utterances using deep neural networks

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