Jitter and Shimmer measurements for speaker diarization

Jitter and shimmer voice quality features have been successfully used to characterize speaker voice traits and detect voice pathologies. Jitter and shimmer measure variations in the fundamental frequency and amplitude of speaker's voice, respectively. Due to their nature, they can be used to assess differences between speakers. In this paper, we investigate the usefulness of these voice quality features in the task of speaker diarization. The combination of voice quality features with the conventional spectral features, Mel-Frequency Cepstral Coefficients (MFCC), is addressed in the framework of Augmented Multiparty Interaction (AMI) corpus, a multi-party and spontaneous speech set of recordings. Both sets of features are independently modeled using mixture of Gaussians and fused together at the score likelihood level. The experiments carried out on the AMI corpus show that incorporating jitter and shimmer measurements to the baseline spectral features decreases the diarization error rate in most of the recordings.Peer ReviewedPostprint (published version

Discriminative features for GMM and i-vector based speaker diarization

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.Postprint (published version

Data-Driven Representation Learning in Multimodal Feature Fusion

abstract: Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction. We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems. In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Detection and handling of overlapping speech for speaker diarization

This thesis concerns the detection of overlapping speech segments and its further application for the improvement of speaker diarization performance. We propose the use of three spatial cross-correlation-based parameters for overlap detection on distant microphone channel data. Spatial features from dierent microphone pairs are fused by means of principal component analysis or by an approach involving a multilayer perceptron. In addition, we investigate the possibility of employing long-term prosodic information. The most suitable subset of candidate prosodic features is determined by a two-step mRMR feature selection algorithm. For segments including detected overlapping speech the speaker diarization system picks a second speaker label, and such segments are also discarded from the model training. The proposed overlap labeling technique is integrated in the Viterbi-decoding part of the diarization algorithm.Peer ReviewedPostprint (published version

Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved

A Comparative Study on multichannel Speaker-attributed automatic speech recognition in Multi-party Meetings

Speaker-attributed automatic speech recognition (SA-ASR) in multiparty meeting scenarios is one of the most valuable and challenging ASR task. It was shown that single-channel frame-level diarization with serialized output training (SC-FD-SOT), single-channel word-level diarization with SOT (SC-WD-SOT) and joint training of single-channel target-speaker separation and ASR (SC-TS-ASR) can be exploited to partially solve this problem. SC-FD-SOT obtains the speaker-attributed transcriptions by aligning the speaker diarization results with the ASR hypotheses, SC-WD-SOT uses word-level diarization to get rid of the alignment dependence on timestamps, and SC-TS-ASR jointly trains target-speaker separation and ASR modules, which achieves the best performance. In this paper, we propose three corresponding multichannel (MC) SA-ASR approaches, namely MC-FD-SOT, MC-WD-SOT and MC-TS-ASR. For different tasks/models, different multichannel data fusion strategies are considered, including channel-level cross-channel attention for MC-FD-SOT, frame-level cross-channel attention for MC-WD-SOT and neural beamforming for MC-TS-ASR. Experimental results on the AliMeeting corpus reveal that our proposed multichannel SA-ASR models can consistently outperform the corresponding single-channel counterparts in terms of the speaker-dependent character error rate (SD-CER)

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations