3,735 research outputs found

    Speaker Segmentation and Clustering in Meetings

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    Robust Unsupervised Speaker Segmentation for Audio Diarization

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    Audio diarization is the process of partitioning an input audio stream into homogeneous regions according to their specific audio sources. These sources can include audio type (speech, music, background noise, ect.), speaker identity and channel characteristics. With the continually increasing number of larges volumes of spoken documents including broadcasts, voice mails, meetings and telephone conversations, diarization has received a great deal of interest in recent years which significantly impacts performances of automatic speech recognition and audio indexing systems. A subtype of audio diarization, where the speech segments of the signal are broken into different speakers, is speaker diarization. It generally answers to the question "Who spoke when?" and it is divided in two modules: speaker segmentation and speaker clustering. This chapter discusses the problem of automatically detecting speaker change points presented in a given audio stream, without prior acoustic information on the speakers. We introduce a new unsupervised speaker segmentation technique based on One Class Support Vector Machines (1-SVMs) robust to different acoustic conditions. We evaluated the robustness improvements of this method by segmenting different types of audio stream (broadcast news, meetings and telephone conversations) and comparing the results with model selection segmentation techniques based on the Bayesian information criterion (BIC)

    Determining the number of speakers in a meeting using microphone array features

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    The accuracy of speaker diarisation in meetings relies heavily on determining the correct number of speakers. In this paper we present a novel algorithm based on time difference of arrival (TDOA) features that aims to find the correct number of active speakers in a meeting and thus aid the speaker segmentation and clustering process. With our proposed method the microphone array TDOA values and known geometry of the array are used to calculate a speaker matrix from which we determine the correct number of active speakers with the aid of the Bayesian information criterion (BIC). In addition, we analyse several well-known voice activity detection (VAD) algorithms and verified their fitness for meeting recordings. Experiments were performed using the NIST RT06, RT07 and RT09 data sets, and resulted in reduced error rates compared with BIC-based approaches. Index Terms — Speaker diarisation in meetings, microphone array, time difference of arrival (TDOA), speech segmentation and clustering, BIC, voice activity detection (VAD) 1

    Speaker Diarization Based on Intensity Channel Contribution

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    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

    Speaker diarization of multi-party conversations using participants role information: political debates and professional meetings

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    Speaker Diarization aims at inferring who spoke when in an audio stream and involves two simultaneous unsupervised tasks: (1) the estimation of the number of speakers, and (2) the association of speech segments to each speaker. Most of the recent efforts in the domain have addressed the problem using machine learning techniques or statistical methods (for a review see [11]) ignoring the fact that the data consists of instances of human conversations

    Automated speech and audio analysis for semantic access to multimedia

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    The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives

    Jitter and Shimmer measurements for speaker diarization

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    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
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