Enhanced speaker recognition based on intra-modal fusion and accent modeling

Mangayyagari, S.; Islam, T.; Sankar, Ravi

doi:10.1109/icpr.2008.4761928

Cited by 5 publications

(3 citation statements)

References 16 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Primarily, accent is as an important soft biometric trait of an individual [11], like age and gender, and, as such, can serve for verification purposes [6], [12]. Accent analysis is also essential for applications such as pronunciation modelling [13] and computer-assisted L2 learning [14].…”

Section: Introductionmentioning

confidence: 99%

Discrimination Between Native and Non-Native Speech Using Visual Features Only

Georgakis

Petridis

Pantić

2016

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Abstract-Accent is a soft biometric trait that can be inferred from pronunciation and articulation patterns characterising the speaking style of an individual. Past research has addressed the task of classifying accent, as belonging to a native language speaker or a foreign language speaker, by means of the audio modality only. However, features extracted from the visual stream of speech have been successfully used to extend or substitute audio-only approaches that target speech or language recognition. Motivated by these findings, we investigate to what extent temporal visual speech dynamics attributed to accent can be modelled and identified when the audio stream is missing or noisy, and the speech content is unknown. We present here a fully automated approach to discriminating native from non-native English speech, based exclusively on visual cues. A systematic evaluation of various appearance and shape features for the target problem is conducted, with the former consistently yielding superior performance. Subject-independent cross-validation experiments are conducted on mobile phone recordings of continuous speech and isolated word utterances spoken by 56 subjects from the challenging MOBIO Database. High performance is achieved on a text-dependent protocol, with the best score of 76.5% yielded by fusion of five HMMs trained on appearance features. Our framework is also efficient even when tested on examples of speech unseen in the training phase, although performing less accurately compared to the text-dependent case.

show abstract

Section: Introductionmentioning

confidence: 99%

Discrimination Between Native and Non-Native Speech Using Visual Features Only

Georgakis

Petridis

Pantić

2016

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…As the speech signal has two types of features, low level and high level features. High level information containsaccent, rhythm, word or phrase usage or pronunciation [18,19]. The low-level information analyzes basic structure of speech signal i.e.…”

mentioning

confidence: 99%

Text Independent Speaker Identification with Prosody Features in Presence of Noise

2019

IJITEE

View full text Add to dashboard Cite

Automatic recognition of Meta data of a speaker apart from recognizing only his or her identity is a challenging task. It gives rich behavioral characteristics of a person.Maximum work have been done in speaker recognitionon low level spectral features. Which gives good accuracy with minimum error, but they ignore other information about the speaker. Also in spectral variations, in session variations and in channel variations these features give degraded performance. State-of-the-art systems for text-independent speaker identification use Mel Frequency cepstral coefficients (MFCCs) as main features. Generally this system performs very good under clean conditions and acceptable under matched conditions. Under mismatched conditions, however, performance significantly deteriorates. One of the principal reasons for poor performance in these conditions is because of the nature of low-level features; being spectral, they are susceptible to spectral variations due to noise and channel effects.Prosodic features are used successfully in these variation conditions as well as in presence of noise.In this paper multi SNR environment is considered. Recognition accuracy has been calculated at different SNR levels i.e. 15 dB, 25 dB and 35 dB.Also results are tested at different types of noise such as Traffic noise, cockpit noise, babble noise and fan noise. It has been found that combining prosodic features such as pitch, energy and formants gives improved performance.

show abstract

“…The knowledge of the accent of a speaker is not useful only as a preprocessing step for speech recognition. Primarily, accent is an important soft biometric trait of an individual [11], such as age and gender, and, as such, can serve for verification purposes [6], [12]. Accent analysis is also essential for applications such as pronunciation modeling [13] and computer-assisted L2 learning [14].…”

mentioning

confidence: 99%

Assessing Loss Event Frequencies of Smart Grid Cyber Threats: Encoding Flexibility into FAIR Using Bayesian Network Approach

Chen

Chai

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Accent is a soft biometric trait that can be inferred from pronunciation and articulation patterns characterizing the speaking style of an individual. Past research has addressed the task of classifying accent, as belonging to a native language speaker or a foreign language speaker, by means of the audio modality only. However, features extracted from the visual stream of speech have been successfully used to extend or substitute audio-only approaches that target speech or language recognition. Motivated by these findings, we investigate to what extent temporal visual speech dynamics attributed to accent can be modeled and identified when the audio stream is missing or noisy, and the speech content is unknown. We present here a fully automated approach to discriminating native from non-native English speech, based exclusively on visual cues. A systematic evaluation of various appearance and shape features for the target problem is conducted, with the former consistently yielding superior performance. Subject-independent cross-validation experiments are conducted on mobile phone recordings of continuous speech and isolated word utterances spoken by 56 subjects from the challenging MOBIO database. High performance is achieved on a text-dependent (TD) protocol, with the best score of 76.5% yielded by fusion of five hidden Markov models trained on appearance features. Our framework is also efficient even when tested on examples of speech unseen in the training phase, although performing less accurately compared to the TD case.

show abstract

Enhanced speaker recognition based on intra-modal fusion and accent modeling

Cited by 5 publications

References 16 publications

Discrimination Between Native and Non-Native Speech Using Visual Features Only

Discrimination Between Native and Non-Native Speech Using Visual Features Only

Text Independent Speaker Identification with Prosody Features in Presence of Noise

Assessing Loss Event Frequencies of Smart Grid Cyber Threats: Encoding Flexibility into FAIR Using Bayesian Network Approach

Contact Info

Product

Resources

About