Automatic dialect identification of extemporaneous conversational, Latin American Spanish speech

Zissman, Marc A.; Gleason, Terry P.; Rekart, Deborah M.; Losiewicz, Beth L.

doi:10.1109/icassp.1996.543236

Cited by 55 publications

(36 citation statements)

References 13 publications

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“…The latter is based on the hypothesis that dialects or accents differ in terms of their phone sequence distributions. It uses phone recognizer outputs, such as n-gram statistics, together with a language modeling back-end [8,9].…”

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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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%

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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“…Research shows that a mismatch in dialects between training and testing speakers significantly influences recognition accuracy in several languages like French (Brousseau and Fox, 1992), Japanese (Kudo et al, 1996), Dutch (Diakolukas et al, 1997), German (Fischer et al, 1998) or English (Chengalvarayan, 2001), as an example. Spanish is not an exception, as it has been shown in research (de la Torre et al, 1996;Zissmanm et al, 1996;Aalburg and Hoege, 2003). Efforts in dialect ASR technology have followed two different goals: (i) to improve dialectal recognition rates by developing recognition systems tailored to specific dialects and (ii) to design multidialectal ASR systems robust to dialect variation.…”

Section: Introductionmentioning

confidence: 76%

Multidialectal Spanish acoustic modeling for speech recognition

Caballero¹,

Moreno²,

Nogueiras³

2009

Speech Communication

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During the last years, language resources for speech recognition have been collected for many languages and specifically, for global languages. One of the characteristics of global languages is their wide geographical dispersion, and consequently, their wide phonetic, lexical, and semantic dialectal variability. Even if the collected data is huge, it is difficult to represent dialectal variants accurately.This paper deals with multidialectal acoustic modeling for Spanish. The goal is to create a set of multidialectal acoustic models that represents the sounds of the Spanish language as spoken in Latin America and Spain. A comparative study of different methods for combining data between dialects is presented. The developed approaches are based on decision tree clustering algorithms. They differ on whether a multidialectal phone set is defined, and in the decision tree structure applied.Besides, a common overall phonetic transcription for all dialects is proposed. This transcription can be used in combination with all the proposed acoustic modeling approaches. Overall transcription combined with approaches based on defining a multidialectal phone set leads to a full dialect-independent recognizer, capable to recognize any dialect even with a total absence of training data from such dialect.Multidialectal systems are evaluated over data collected in five different countries: Spain, Colombia, Venezuela, Argentina and Mexico. The best results given by multidialectal systems show a relative improvement of 13% over the results obtained with monodialectal systems. Experiments with dialect-independent systems have been conducted to recognize speech from Chile, a dialect not seen in the training process. The recognition results obtained for this dialect are similar to the ones obtained for other dialects.

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“…An early example of the latter is Zissman's work [10] on the application of Phone Recognition followed by Language Modelling (PRLM) to accent recognition. The performance of PRLM can be further improved by the use of discriminative methods that focus on phones or phone sequences that are characteristic of an accent [7,11].…”

Section: Language Dialect and Accents In Speech And Language Technologymentioning

confidence: 99%

Human and computer recognition of regional accents and ethnic groups from British English speech

Hanani

Russell

Carey

2013

Computer Speech & Language

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Language Recognition Evaluation (LRE) tasks. This system is applied to the problems of regional accent recognition for British English, and ethnic group recognition within a particular accent. We compare the results with human performance and, for accent recognition, the 'text dependent' ACCDIST accent recognition measure. For the fourteen regional accents of British English in the ABI-1 corpus (good quality read speech), our language ID system achieves a recognition accuracy of 86.4%, compared with 95.18% for our best ACCDIST-based system and 58.24% for human listeners. The "Voices across Birmingham" corpus contains significant amounts of telephone conversational speech for the two largest ethnic groups in the city of Birmingham (UK), namely the 'Asian' and 'White' communities. Our language ID system distinguishes between these two groups with an accuracy of 94.3% compared with 90.24% for human listeners. Although direct comparison is difficult, it seems that our language ID system performs much better on the standard twelve class NIST 2003 Language Recognition Evaluation task or the two class ethnic group recognition task than on the fourteen class regional accent recognition task. We conclude that automatic accent recognition is a challenging task for speech technology, and that the use of natural conversational speech may be advantageous for these types of paralinguistic task.One issue with conventional approaches to language ID that use high-order Gaussian Mixture Models (GMMs) and high-dimensional feature vectors is the amount of computing power that they require. Currently, multi-core Graphics Processing Units (GPUs) provide a possible solution at very little cost. In this thesis we also explore the application of GPUs to speech signal and pattern processing, using language ID as a vehicle to demonstrate their benefits. Realisation of the full potential of GPUs requires both effective coding of predetermined algorithms, and, in cases where there is a choice, selection of the algorithm or technique for a specific function that is most able to exploit the properties of the GPU. We demonstrate these principles using the NIST LRE 2003 task, which involves processing over 600 hours of speech. We focus on two parts of the system, namely the acoustic classifier, which is based on a 2048 component GMM, and the acoustic feature extraction process. In the case of the latter we compare a conventional FFT-based analysis with an FIR filter bank, both in terms of their ability to exploit the GPU architecture and language ID performance. With no increase in error rate our GPU based system, with an FIR-based front-end, completes the full NIST LRE 2003 task in 16 hours, compared with 180 hours for the more conventional FFT-based system on a standard CPU (a speed up factor of more than 11).

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Automatic dialect identification of extemporaneous conversational, Latin American Spanish speech

Cited by 55 publications

References 13 publications

Introducing attribute features to foreign accent recognition

Introducing attribute features to foreign accent recognition

Multidialectal Spanish acoustic modeling for speech recognition

Human and computer recognition of regional accents and ethnic groups from British English speech

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