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

Speaker Diarization Based on Intensity Channel Contribution

The time delay of arrival (TDOA) between multiple microphones has been used since 2006 as a source of information (localization) to complement the spectral features for speaker diarization. In this paper, we propose a new localization feature, the intensity channel contribution (ICC) based on the relative energy of the signal arriving at each channel compared to the sum of the energy of all the channels. We have demonstrated that by joining the ICC features and the TDOA features, the robustness of the localization features is improved and that the diarization error rate (DER) of the complete system (using localization and spectral features) has been reduced. By using this new localization feature, we have been able to achieve a 5.2% DER relative improvement in our development data, a 3.6% DER relative improvement in the RT07 evaluation data and a 7.9% DER relative improvement in the last year's RT09 evaluation data

Selection of TDOA Parameters for MDM Speaker Diarization

Several methods to improve multiple distant microphone (MDM) speaker diarization based on Time Delay of Arrival (TDOA) features are evaluated in this paper. All of them avoid the use of a single reference channel to calculate the TDOA values and, based on different criteria, select among all possible pairs of microphones a set of pairs that will be used to estimate the TDOA's. The evaluated methods have been named the "Dynamic Margin" (DM), the "Extreme Regions" (ER), the "Most Common" (MC), the "Cross Correlation" (XCorr) and the "Principle Component Analysis" (PCA). It is shown that all methods improve the baseline results for the development set and four of them improve also the results for the evaluation set. Improvements of 3.49% and 10.77% DER relative are obtained for DM and ER respectively for the test set. The XCorr and PCA methods achieve an improvement of 36.72% and 30.82% DER relative for the test set. Moreover, the computational cost for the XCorr method is 20% less than the baseline

Influence of transition cost in the segmentation stage of speaker diarization

In any speaker diarization system there is a segmentation phase and a clustering phase. Our system uses them in a single step in which segmentation and clustering are used iteratively until certain condition is met. In this paper we propose an improvement of the segmentation method that cancels a penalization that had been applied in previous works to any transition between speakers. We also study the performance when transitions between speakers are favoured instead of penalized. This last option achieves better results both for the development set (21.65 % relative speaker error improvementSER) and for the test set (4.60% relative speaker error improvement

The use of long-term features for GMM- and i-vector-based speaker diarization systems

Several factors contribute to the performance of speaker diarization systems. For instance, the appropriate selection of speech features is one of the key aspects that affect speaker diarization systems. The other factors include the techniques employed to perform both segmentation and clustering. While the static mel frequency cepstral coefficients are the most widely used features in speech-related tasks including speaker diarization, several studies have shown the benefits of augmenting regular speech features with the static ones. In this work, we have proposed and assessed the use of voice-quality features (i.e., jitter, shimmer, and Glottal-to-Noise Excitation ratio) within the framework of speaker diarization. These acoustic attributes are employed together with the state-of-the-art short-term cepstral and long-term prosodic features. Additionally, the use of delta dynamic features is also explored separately both for segmentation and bottom-up clustering sub-tasks. The combination of the different feature sets is carried out at several levels. At the feature level, the long-term speech features are stacked in the same feature vector. At the score level, the short- and long-term speech features are independently modeled and fused at the score likelihood level. Various feature combinations have been applied both for Gaussian mixture modeling and i-vector-based speaker diarization systems. The experiments have been carried out on Augmented Multi-party Interaction meeting corpus. The best result, in terms of diarization error rate, is reported by using i-vector-based cosine-distance clustering together with a signal parameterization consisting of a combination of static cepstral coefficients, delta, voice-quality, and prosodic features. The best result shows about 24% relative diarization error rate improvement compared to the baseline system which is based on Gaussian mixture modeling and short-term static cepstral coefficients.Peer ReviewedPostprint (published version

Speaker Diarization Features: The UPM Contribution to the RT09 Evaluation

Two new features have been proposed and used in the Rich Transcription Evaluation 2009 by the Universidad Politécnica de Madrid, which outperform the results of the baseline system. One of the features is the intensity channel contribution, a feature related to the location of the speaker. The second feature is the logarithm of the interpolated fundamental frequency. It is the first time that both features are applied to the clustering stage of multiple distant microphone meetings diarization. It is shown that the inclusion of both features improves the baseline results by 15.36% and 16.71% relative to the development set and the RT 09 set, respectively. If we consider speaker errors only, the relative improvement is 23% and 32.83% on the development set and the RT09 set, respectively

Tuning-Robust Initialization Methods for Speaker Diarization

This paper investigates a typical speaker diarization system regarding its robustness against initialization parameter variation and presents a method to reduce manual tuning of these values significantly. The behavior of an agglomerative hierarchical clustering system is studied to determine which initialization parameters impact accuracy most. We show that the accuracy of typical systems is indeed very sensitive to the values chosen for the initialization parameters and factors such as the duration of speech in the recording. We then present a solution that reduces the sensitivity of the initialization values and therefore reduces the need for manual tuning significantly while at the same time increasing the accuracy of the system. For short meetings extracted from the previous (2006, 2007, and 2009) National Institute of Standards and Technology (NIST) Rich Transcription (RT) evaluation data, the decrease of the diarization error rate is up to 50% relative. The approach consists of a novel initialization parameter estimation method for speaker diarization that uses agglomerative clustering with Bayesian information criterion (BIC) and Gaussian mixture models (GMMs) of frame-based cepstral features (MFCCs). The estimation method balances the relationship between the optimal value of the seconds of speech data per Gaussian and the duration of the speech data and is combined with a novel nonuniform initialization method. This approach results in a system that performs better than the current ICSI baseline engine on datasets of the NIST RT evaluations of the years 2006, 2007, and 2009

Speech Recognition in noisy environment using Deep Learning Neural Network

Recent researches in the field of automatic speaker recognition have shown that methods based on deep learning neural networks provide better performance than other statistical classifiers. On the other hand, these methods usually require adjustment of a significant number of parameters. The goal of this thesis is to show that selecting appropriate value of parameters can significantly improve speaker recognition performance of methods based on deep learning neural networks. The reported study introduces an approach to automatic speaker recognition based on deep neural networks and the stochastic gradient descent algorithm. It particularly focuses on three parameters of the stochastic gradient descent algorithm: the learning rate, and the hidden and input layer dropout rates. Additional attention was devoted to the research question of speaker recognition under noisy conditions. Thus, two experiments were conducted in the scope of this thesis. The first experiment was intended to demonstrate that the optimization of the observed parameters of the stochastic gradient descent algorithm can improve speaker recognition performance under no presence of noise. This experiment was conducted in two phases. In the first phase, the recognition rate is observed when the hidden layer dropout rate and the learning rate are varied, while the input layer dropout rate was constant. In the second phase of this experiment, the recognition rate is observed when the input layers dropout rate and learning rate are varied, while the hidden layer dropout rate was constant. The second experiment was intended to show that the optimization of the observed parameters of the stochastic gradient descent algorithm can improve speaker recognition performance even under noisy conditions. Thus, different noise levels were artificially applied on the original speech signal