Prosodic parameter for speaker identification

Bartkova, Katarina; Gac, David Le; Charlet, Delphine; Jouvet, Denis

doi:10.21437/icslp.2002-325

Cited by 5 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This feature was calculated as the F I G U R E 6 Variation in dynamics of F 0 contour of four different male speakers while uttering the text She had your dark suit in greasy wash water all year (taken from the TIMIT database) F I G U R E 7 Representation of the F 0 contour at the syllablelevel using the tilt parameters for the text The viewpoint overlooked the ocean from the TIMIT database. The numbering on the top indicates the syllable position difference between the maximum and minimum short time energies within a syllable to represent different stress patterns [42,51].…”

Section: Intensity Featuresmentioning

confidence: 99%

“…The dynamics of prosodic parameters, such as duration, intensity, and pitch, can reveal more about the characteristics of an individual and his speaking style [42]. Figure 5 illustrates these prosodic variations in speech waveforms of four different speakers (i.e., two female and two male) uttering the same text.…”

Section: Significance Of Prosody For Speaker Recognitionmentioning

confidence: 99%

“…Motivated by the fact that different speaking styles can affect the syllable energy distribution, we tried herein to include the rate of change of short time energy as a feature. This feature was calculated as the difference between the maximum and minimum short time energies within a syllable to represent different stress patterns [42, 51].…”

Section: Parametrization Of Prosody For Speaker Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

Variational autoencoder for prosody‐based speaker recognition

Alex¹,

Mary²

2023

ETRI Journal

View full text Add to dashboard Cite

This paper describes a novel end‐to‐end deep generative model‐based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker‐specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable‐like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency ( ), are then extracted at the syllable level and used to train/adapt a speaker‐dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender‐dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.

show abstract

Section: Intensity Featuresmentioning

confidence: 99%

Section: Significance Of Prosody For Speaker Recognitionmentioning

confidence: 99%

Section: Parametrization Of Prosody For Speaker Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Variational autoencoder for prosody‐based speaker recognition

Alex¹,

Mary²

2023

ETRI Journal

View full text Add to dashboard Cite

show abstract

“…However, other levels of information play an important role in the human recognition process, which means that they convey useful speaker information. Therefore, there is reason to believe, as recent studies have demonstrated (Carey et al, 1996;Sönmez et al, 1998;Doddington, 2001;Bartkova et al, 2002;Weber et al, 2002;, that these levels can add complementary knowledge to the traditional spectrum-based recognition systems, improving their accuracy. Moreover, they appear to be more robust to acoustic degradations from channel and noise effects (Atal, 1972;Carey et al, 1996).…”

Section: Motivation and Objectivesmentioning

confidence: 99%

“…However, there are other levels of information that convey useful information about the speaker. Recent studies (Carey et al, 1996;Sönmez et al, 1998;Doddington, 2001;Bartkova et al, 2002;Weber et al, 2002; have demonstrated that these levels can add complementary knowledge to the traditional spectrum-based recognition systems, improving their accuracy.…”

Section: Citar [Oppenheim "History Of Cepstrum"]mentioning

confidence: 99%

Fusing prosodic and acoustic information for speaker recognition

Farrús Cabeceran

View full text Add to dashboard Cite

El reconeixement automàtic del locutor és la utilització d’una màquina per identificar un individu a partir de d’un missatge parlat. Recentment, aquesta tecnologia ha experimentat un increment en l’ús de diverses aplicacions com el control d’accés, l’autenticació de transaccions, la cooperació amb la justícia, l’analítica forense o la personalització de serveis, entre d’altres. Una de les qüestions centrals que es tracten en aquest camp és el fet de saber quina part del senyal de veu conté informació del locutor. Tradicionalment, els sistemes de reconeixement automàtic del locutor s’han basat principalment en característiques relacionades amb l’espectre de la veu. No obstant, els humans utilitzen altres fonts d’informació per reconèixer locutors, de manera que hi ha motius per pensar que aquestes fonts poden tenir un paper important en la tasca de reconeixement automàtic del locutor, aportar coneixement complementari als sistemes de tradicionals basats en l’espectre de la veu i millorar-ne la precisió. L’objectiu principal d’aquesta tesi és incorporar informació prosòdica a un sistema espectral tradicional per tal de millorar-ne el funcionament. Amb aquesta finalitat, diverses característiques relacionades amb la prosòdia – constituïda per elements d’entonació, ritme i accent – es seleccionen i es combinen amb les característiques espectrals existents. A més a més, la tesi també es centra en la utilització de característiques acústiques addicionals – a saber, jitter i shimmer – per millorar el funcionament del sistema de verificació espectral-prosòdic proposat. Totes dues característiques estan relacionades amb la forma i dimensió del tracte vocal, i s’han utilitzat en gran part per detectar patologies de la veu. La majoria d’aplicacions que s’han esmentat abans es poden utilitzar en un entorn multimodal; per aquest motiu, les característiques de veu utilitzades en el sistema de reconeixement del locutor també es combinen amb altres identificadors biomètrics – concretament, la cara – per tal de millorar el funcionament global del sistema. Amb aquest objectiu, s’utilitzen diverses tècniques de normalització i de fusió, i els resultats de la fusió final es milloren aplicant diferents estratègies de fusió basades en seqüències de passos. A més a més, la fusió multimodal també es millora aplicant una equalització d’histogrames com a tècnica de normalització a les distribucions de puntuacions unimodals. Per altra banda, és sabut que els humans poden identificar els altres a partir de la veu fins i tot quan aquestes veus estan alterades d’alguna manera. La qüestió rau en quina mesura els sistemes automàtics de reconeixement del locutor són vulnerables a les diferents alteracions de la veu, com ara la imitació humana o la conversió artificial. L’última part de la tesi consisteix en una anàlisi de la robustesa d’aquests sistemes a les imitacions de veu humanes i a les veus convertides sintèticament, i de la influència dels accents estrangers – com a tipus d’imitació – en el reconeixement auditiu del locutor. Automatic speaker recognition is the use of a machine to identify an individual from a spoken sentence. Recently, this technology has been undergone an increasing use in applications such as access control, transaction authentication, law enforcement, forensics, and system customisation, among others. One of the central questions addressed by this field is what is it in the speech signal that conveys speaker identity. Traditionally, automatic speaker recognition systems have relied mostly on short-term features related to the spectrum of the voice. However, human speaker recognition relies on other sources of information; therefore, there is reason to believe that these sources can play also an important role in the automatic speaker recognition task, adding complementary knowledge to the traditional spectrum-based recognition systems and thus improving their accuracy. The main objective of this thesis is to add prosodic information to a traditional spectral system in order to improve its performance. To this end, several characteristics related to human speech prosody – which is conveyed through intonation, rhythm and stress – are selected and combined them with the existing spectral features. Furthermore, this thesis also focuses on the use of additional acoustic features – namely jitter and shimmer – to improve the performance of the proposed spectral-prosodic verification system. Both features are related to the shape and dimension of the vocal tract, and they have been largely used to detect voice pathologies. Since almost all the above-mentioned applications can be used in a multimodal environment, this thesis also aims to combine the voice features used in the speaker recognition system together with other biometric identifiers – face – in order to improve the global performance. To this end, several normalisation and fusion techniques are used, and the final fusion results are improved by applying different fusion strategies based on sequences of several steps. Furthermore, multimodal fusion is also improved by applying a histogram equalisation to the unimodal score distributions as a normalisation technique. On the other hand, it is well know that humans are able to identify others from voice even when their voices are disguised. The question arises as to how vulnerable automatic speaker recognition systems are against different voice disguises, such as human imitation or artificial voice conversion, which are potential threats to security systems that rely on automatic speaker recognition. The last part of this thesis finishes with an analysis of the robustness of such systems against human voice imitations and synthetic converted voices, and the influence of foreign accents and dialects – as a sort of imitation – in auditory speaker recognition.

show abstract