Using voice-quality measurements with prosodic and spectral features for speaker diarization

Woubie, Abraham; Luque, Jordi; Hernando, Javier

doi:10.21437/interspeech.2015-110

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…Moreover, using jitter and shimmer measurements together with cepstral ones improves the classification accuracy of different speaking styles [17]. Such voice-quality features are also important in speaker diarization [20], and they can be used to characterize different types of voices such as breathy, tense, harsh, whispery and creaky [16].…”

Section: Introductionmentioning

confidence: 99%

Voice Quality Features for Replay Attack Detection

Woubie

Bäckström

2022

2022 30th European Signal Processing Conference (EUSIPCO)

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Replay attacks are mainly carried out to get fraudulent access to an Automatic Speaker Verification (ASV) system. These type of attack requires recording and playback devices. In this paper, we investigate the usefulness of voice-quality features to detect replay attacks. The voice-quality features are used together with the state-of-the-art constant Q cepstral coefficients (CQCC) features. The two feature sets are fused at the score level. Thus, the log-likelihood scores estimated from the two feature sets are linearly weighted to obtain a single fused score. The fused score is used to classify whether a given speech sample is genuine or spoofed. Our experiments with the ASVspoof 2017 dataset demonstrate that the fusion of log-likelihood scores extracted from the CQCC and voice quality features provide better Equal Error Rate (EER) compared to the baseline system which is based only on CQCC features.

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Section: Introductionmentioning

confidence: 99%

Voice Quality Features for Replay Attack Detection

Woubie

Bäckström

2022

2022 30th European Signal Processing Conference (EUSIPCO)

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“…Short-term features are extracted from a single speech frame, while long-term features are extracted from portions of speech longer than one frame. Since long-term features provide discriminative power, fusion of short-term spectral features with long-term features has been applied on different speech applications [3]- [5]. Longterm speech features are also robust to channel variation since temporal patterns do not change with the change of acoustic conditions [6].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, using jitter and shimmer measurements together with cepstral ones improves the classification accuracy of different speaking styles [8]. Such voice-quality features are also important in speaker diarization [5], [11]- [13], and they can be used to characterize different types of voices such as breathy, tense, harsh, whispery and creaky [7].…”

Section: Introductionmentioning

confidence: 99%

Voice-quality Features for Deep Neural Network Based Speaker Verification Systems

Woubie

Koivisto

Bäckström

2021

2021 29th European Signal Processing Conference (EUSIPCO)

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Jitter and shimmer are voice-quality features which have been successfully used to detect voice pathologies and classify different speaking styles. In this paper, we investigate the usefulness of such voice-quality features in neural-network based speaker verification systems. To combine these two sets of features, the cosine distance scores estimated from the two sets are linearly weighted to obtain a single, fused score. The fused score is used to accept/reject a given speaker. The experimental results carried out on Voxceleb-1 dataset demonstrate that the fusion of the cosine distance scores extracted from the melspectrogram and voice quality features provide a 15% relative improvement in Equal Error Rate (EER) compared to the baseline system which is based only on mel-spectrogram features.

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“…Experimental results show that the best results are obtained by fusing the voice-quality features with the prosodic ones at the feature level, and then fusing them with the cepstral features at the score level. The results of this work has been published in [Woubie et al, 2015].…”

Section: Using Voice-quality Measurements With Prosodic and Spectral ...mentioning

confidence: 99%

“…Since fusion techniques extract multiple information from multiple sources and improve accuracy, they have been successfully used in various tasks including speaker recognition [Farrús et al, 2007] , speaker diarization [Friedland et al, 2009, Zelenák and Hernando, Chapter 5. Proposed Speaker Diarization Systems 2011, Woubie et al, 2015 and multi-biometrics [Nandakumar et al, 2009, Nandakumar et al, 2008.…”

Section: Fusion Techniquesmentioning

confidence: 99%

Discriminative features for GMM and i-vector based speaker diarization

Zewoudie

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Speaker diarization has received several research attentions over the last decade. Among the different domains of speaker diarization, diarization in meeting domain is the most challenging one. It usually contains spontaneous speech and is, for example, susceptible to reverberation. The appropriate selection of speech features is one of the factors that affect the performance of speaker diarization systems. Mel Frequency Cepstral Coefficients (MFCC) are the most widely used short-term speech features in speaker diarization. Other factors that affect the performance of speaker diarization systems are the techniques employed to perform both speaker segmentation and speaker clustering. In this thesis, we have proposed the use of jitter and shimmer long-term voice-quality features both for Gaussian Mixture Modeling (GMM) and i-vector based speaker diarization systems. The voice-quality features are used together with the state-of-the-art short-term cepstral and long-term speech ones. The long-term features consist of prosody and Glottal-to-Noise excitation ratio (GNE) descriptors. Firstly, the voice-quality, prosodic and GNE features are stacked in the same feature vector. Then, they are fused with cepstral coefficients at the score likelihood level both for the proposed Gaussian Mixture Modeling (GMM) and i-vector based speaker diarization systems. For the proposed GMM based speaker diarization system, independent HMM models are estimated from the short-term and long-term speech feature sets. The fusion of the short-term descriptors with the long-term ones in speaker segmentation is carried out by linearly weighting the log-likelihood scores of Viterbi decoding. In the case of speaker clustering, the fusion of the short-term cepstral features with the long-term ones is carried out by linearly fusing the Bayesian Information Criterion (BIC) scores corresponding to these feature sets. For the proposed i-vector based speaker diarization system, the speaker segmentation is carried out exactly the same as in the previously mentioned GMM based speaker diarization system. However, the speaker clustering technique is based on the recently introduced factor analysis paradigm. Two set of i-vectors are extracted from the speaker segmentation hypothesis. Whilst the first i-vector is extracted from short-term cepstral features, the second one is extracted from the voice quality, prosody and GNE descriptors. Then, the cosine-distance and Probabilistic Linear Discriminant Analysis (PLDA) scores of i-vectors are linearly weighted to obtain a fused similarity score. Finally, the fused score is used as speaker clustering distance. We have also proposed the use of delta dynamic features for speaker clustering. The motivation for using deltas in clustering is that delta dynamic features capture the transitional characteristics of the speech signal which contain speaker specific information. This information is not captured by the static cepstral coefficients. The delta features are used together with the short-term static cepstral coefficients and long-term speech features (i.e., voice-quality, prosody and GNE) both for GMM and i-vector based speaker diarization systems. The experiments have been carried out on Augmented Multi-party Interaction (AMI) meeting corpus. The experimental results show that the use of voice-quality, prosody, GNE and delta dynamic features improve the performance of both GMM and i-vector based speaker diarization systems. La diarización del altavoz ha recibido varias atenciones de investigación durante la última década. Entre los diferentes dominios de la diarización del hablante, la diarización en el dominio del encuentro es la más difícil. Normalmente contiene habla espontánea y, por ejemplo, es susceptible de reverberación. La selección apropiada de las características del habla es uno de los factores que afectan el rendimiento de los sistemas de diarización de los altavoces. Los Coeficientes Cepstral de Frecuencia Mel (MFCC) son las características de habla de corto plazo más utilizadas en la diarización de los altavoces. Otros factores que afectan el rendimiento de los sistemas de diarización del altavoz son las técnicas empleadas para realizar tanto la segmentación del altavoz como el agrupamiento de altavoces. En esta tesis, hemos propuesto el uso de jitter y shimmer características de calidad de voz a largo plazo tanto para GMM y i-vector basada en sistemas de diarización de altavoces. Las características de calidad de voz se utilizan junto con el estado de la técnica a corto plazo cepstral y de larga duración de habla. Las características a largo plazo consisten en la prosodia y los descriptores de relación de excitación Glottal-a-Ruido (GNE). En primer lugar, las características de calidad de voz, prosódica y GNE se apilan en el mismo vector de características. A continuación, se fusionan con coeficientes cepstrales en el nivel de verosimilitud de puntajes tanto para los sistemas de diarización de altavoces basados ¿¿en el modelo Gaussian Mixture Modeling (GMM) como en los sistemas basados ¿¿en i-vector. . Para el sistema de diarización de altavoces basado en GMM propuesto, se calculan modelos HMM independientes a partir de cada conjunto de características. En la segmentación de los altavoces, la fusión de los descriptores a corto plazo con los de largo plazo se lleva a cabo mediante la ponderación lineal de las puntuaciones log-probabilidad de decodificación Viterbi. En la agrupación de altavoces, la fusión de las características cepstrales a corto plazo con las de largo plazo se lleva a cabo mediante la fusión lineal de las puntuaciones Bayesian Information Criterion (BIC) correspondientes a estos conjuntos de características. Para el sistema de diarización de altavoces basado en un vector i, la fusión de características se realiza exactamente igual a la del sistema basado en GMM antes mencionado. Sin embargo, la técnica de agrupación de altavoces se basa en el paradigma de análisis de factores recientemente introducido. Dos conjuntos de i-vectores se extraen de la hipótesis de segmentación de altavoz. Mientras que el primer vector i se extrae de características espectrales a corto plazo, el segundo se extrae de los descriptores de calidad de voz apilados, prosódicos y GNE. A continuación, las puntuaciones de coseno-distancia y Probabilistic Linear Discriminant Analysis (PLDA) entre i-vectores se ponderan linealmente para obtener una puntuación de similitud fundida. Finalmente, la puntuación fusionada se utiliza como distancia de agrupación de altavoces. También hemos propuesto el uso de características dinámicas delta para la agrupación de locutores. La motivación para el uso de deltas en la agrupación es que las características dinámicas delta capturan las características de transición de la señal de voz que contienen información específica del locutor. Esta información no es capturada por los coeficientes cepstrales estáticos. Las características delta se usan junto con los coeficientes cepstrales estáticos a corto plazo y las características de voz a largo plazo (es decir, calidad de voz, prosodia y GNE) tanto para sistemas de diarización de altavoces basados en GMM como en sistemas i-vector. Los resultados experimentales sobre AMI muestran que el uso de calidad vocal, prosódica, GNE y dinámicas delta mejoran el rendimiento de los sistemas de diarización de altavoces basados en GMM e i-vector.

show abstract

Using voice-quality measurements with prosodic and spectral features for speaker diarization

Cited by 9 publications

References 18 publications

Voice Quality Features for Replay Attack Detection

Voice Quality Features for Replay Attack Detection

Voice-quality Features for Deep Neural Network Based Speaker Verification Systems

Discriminative features for GMM and i-vector based speaker diarization

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