Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

Latent Class Model with Application to Speaker Diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. 1) Adding neighbor windows to extract more speaker information for each short segment. 2) Using a hidden Markov model to avoid frequent speaker change points. 3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance

Review of Research on Speech Technology: Main Contributions From Spanish Research Groups

In the last two decades, there has been an important increase in research on speech technology in Spain, mainly due to a higher level of funding from European, Spanish and local institutions and also due to a growing interest in these technologies for developing new services and applications. This paper provides a review of the main areas of speech technology addressed by research groups in Spain, their main contributions in the recent years and the main focus of interest these days. This description is classified in five main areas: audio processing including speech, speaker characterization, speech and language processing, text to speech conversion and spoken language applications. This paper also introduces the Spanish Network of Speech Technologies (RTTH. Red Temática en Tecnologías del Habla) as the research network that includes almost all the researchers working in this area, presenting some figures, its objectives and its main activities developed in the last years

Optimized Speaker Diarization System using Discrete Wavelet Transform and Pyknogram

The aim of this paper is to present an optimized speaker diarization system that efficiently detects speaker change points in multispeaker speech data. Speaker diarization is the process to detect speaker turns and group together segments uttered by the same speaker. It can be used in speaker recognition, audio information retrieval, audio transcription, audio clustering, indexing and captioning of TV shows and movies. In this proposed technique, the daubechies 40-wavelet transform is used to compress the audio stream in the ratio of 1:4; their features are extracted by enhanced spectrogram called pyknogram based on Teaser Kaiser Energy Operator (TKEO). This method relies on resonances (formants) and harmonic structure of speech which are enhanced by decomposing the spectral sub-bands into amplitude and frequency components. The weighted average of the instantaneous frequency components are used to derive a short-time estimate value for the dominant frequency in each subband over a fixed period of time 0.12msec. Sudden changes in the dominant frequency correspond to the speaker change point and are detected by using traditional delta Bayesian Information Criteria (?BIC). This technique do not uses voice activity detection process (VAD). For re-segmentation, Information Change Rate (ICR) is used. Finally, hierarchical clustering algorithm make groups of homogeneous segments and are plotted by Dendrogram function in Matlab. The results are evaluated by F-measure and diarization error rate. It shows that the proposed method gives fast and better results as compared to traditional method with Mel frequency cepstral coefficient (MFCC) and Bayesian Information Criteria (BIC) algorithms

Robust speaker diarization for meetings

Aquesta tesi doctoral mostra la recerca feta en l'àrea de la diarització de locutor per a sales de reunions. En la present s'estudien els algorismes i la implementació d'un sistema en diferit de segmentació i aglomerat de locutor per a grabacions de reunions a on normalment es té accés a més d'un micròfon per al processat. El bloc més important de recerca s'ha fet durant una estada al International Computer Science Institute (ICSI, Berkeley, Caligornia) per un període de dos anys.La diarització de locutor s'ha estudiat força per al domini de grabacions de ràdio i televisió. La majoria dels sistemes proposats utilitzen algun tipus d'aglomerat jeràrquic de les dades en grups acústics a on de bon principi no se sap el número de locutors òptim ni tampoc la seva identitat. Un mètode molt comunment utilitzat s'anomena "bottom-up clustering" (aglomerat de baix-a-dalt), amb el qual inicialment es defineixen molts grups acústics de dades que es van ajuntant de manera iterativa fins a obtenir el nombre òptim de grups tot i acomplint un criteri de parada. Tots aquests sistemes es basen en l'anàlisi d'un canal d'entrada individual, el qual no permet la seva aplicació directa per a reunions. A més a més, molts d'aquests algorisms necessiten entrenar models o afinar els parameters del sistema usant dades externes, el qual dificulta l'aplicabilitat d'aquests sistemes per a dades diferents de les usades per a l'adaptació.La implementació proposada en aquesta tesi es dirigeix a solventar els problemes mencionats anteriorment. Aquesta pren com a punt de partida el sistema existent al ICSI de diarització de locutor basat en l'aglomerat de "baix-a-dalt". Primer es processen els canals de grabació disponibles per a obtindre un sol canal d'audio de qualitat major, a més dínformació sobre la posició dels locutors existents. Aleshores s'implementa un sistema de detecció de veu/silenci que no requereix de cap entrenament previ, i processa els segments de veu resultant amb una versió millorada del sistema mono-canal de diarització de locutor. Aquest sistema ha estat modificat per a l'ús de l'informació de posició dels locutors (quan es tingui) i s'han adaptat i creat nous algorismes per a que el sistema obtingui tanta informació com sigui possible directament del senyal acustic, fent-lo menys depenent de les dades de desenvolupament. El sistema resultant és flexible i es pot usar en qualsevol tipus de sala de reunions pel que fa al nombre de micròfons o la seva posició. El sistema, a més, no requereix en absolute dades d´entrenament, sent més senzill adaptar-lo a diferents tipus de dades o dominis d'aplicació. Finalment, fa un pas endavant en l'ús de parametres que siguin mes robusts als canvis en les dades acústiques. Dos versions del sistema es van presentar amb resultats excel.lents a les evaluacions de RT05s i RT06s del NIST en transcripció rica per a reunions, a on aquests es van avaluar amb dades de dos subdominis diferents (conferencies i reunions). A més a més, es fan experiments utilitzant totes les dades disponibles de les evaluacions RT per a demostrar la viabilitat dels algorisms proposats en aquesta tasca.This thesis shows research performed into the topic of speaker diarization for meeting rooms. It looks into the algorithms and the implementation of an offline speaker segmentation and clustering system for a meeting recording where usually more than one microphone is available. The main research and system implementation has been done while visiting the International Computes Science Institute (ICSI, Berkeley, California) for a period of two years. Speaker diarization is a well studied topic on the domain of broadcast news recordings. Most of the proposed systems involve some sort of hierarchical clustering of the data into clusters, where the optimum number of speakers of their identities are unknown a priory. A very commonly used method is called bottom-up clustering, where multiple initial clusters are iteratively merged until the optimum number of clusters is reached, according to some stopping criterion. Such systems are based on a single channel input, not allowing a direct application for the meetings domain. Although some efforts have been done to adapt such systems to multichannel data, at the start of this thesis no effective implementation had been proposed. Furthermore, many of these speaker diarization algorithms involve some sort of models training or parameter tuning using external data, which impedes its usability with data different from what they have been adapted to.The implementation proposed in this thesis works towards solving the aforementioned problems. Taking the existing hierarchical bottom-up mono-channel speaker diarization system from ICSI, it first uses a flexible acoustic beamforming to extract speaker location information and obtain a single enhanced signal from all available microphones. It then implements a train-free speech/non-speech detection on such signal and processes the resulting speech segments with an improved version of the mono-channel speaker diarization system. Such system has been modified to use speaker location information (then available) and several algorithms have been adapted or created new to adapt the system behavior to each particular recording by obtaining information directly from the acoustics, making it less dependent on the development data.The resulting system is flexible to any meetings room layout regarding the number of microphones and their placement. It is train-free making it easy to adapt to different sorts of data and domains of application. Finally, it takes a step forward into the use of parameters that are more robust to changes in the acoustic data. Two versions of the system were submitted with excellent results in RT05s and RT06s NIST Rich Transcription evaluations for meetings, where data from two different subdomains (lectures and conferences) was evaluated. Also, experiments using the RT datasets from all meetings evaluations were used to test the different proposed algorithms proving their suitability to the task.Postprint (published version

Detection and handling of overlapping speech for speaker diarization

For the last several years, speaker diarization has been attracting substantial research attention as one of the spoken language technologies applied for the improvement, or enrichment, of recording transcriptions. Recordings of meetings, compared to other domains, exhibit an increased complexity due to the spontaneity of speech, reverberation effects, and also due to the presence of overlapping speech. Overlapping speech refers to situations when two or more speakers are speaking simultaneously. In meeting data, a substantial portion of errors of the conventional speaker diarization systems can be ascribed to speaker overlaps, since usually only one speaker label is assigned per segment. Furthermore, simultaneous speech included in training data can eventually lead to corrupt single-speaker models and thus to a worse segmentation. 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-correlationbased parameters for overlap detection on distant microphone channel data. Spatial features from different microphone pairs are fused by means of principal component analysis, linear discriminant analysis, or by a multi-layer perceptron. In addition, we also investigate the possibility of employing longterm prosodic information. The most suitable subset from a set of candidate prosodic features is determined in two steps. Firstly, a ranking according to mRMR criterion is obtained, and then, a standard hill-climbing wrapper approach is applied in order to determine the optimal number of features. The novel spatial as well as prosodic parameters are used in combination with spectral-based features suggested previously in the literature. In experiments conducted on AMI meeting data, we show that the newly proposed features do contribute to the detection of overlapping speech, especially on data originating from a single recording site. In speaker diarization, for segments including detected speaker overlap, a second speaker label is picked, and such segments are also discarded from the model training. The proposed overlap labeling technique is integrated in Viterbi decoding, a part of the diarization algorithm. During the system development it was discovered that it is favorable to do an independent optimization of overlap exclusion and labeling with respect to the overlap detection system. We report improvements over the baseline diarization system on both single- and multi-site AMI data. Preliminary experiments with NIST RT data show DER improvement on the RT ¿09 meeting recordings as well. The addition of beamforming and TDOA feature stream into the baseline diarization system, which was aimed at improving the clustering process, results in a bit higher effectiveness of the overlap labeling algorithm. A more detailed analysis on the overlap exclusion behavior reveals big improvement contrasts between individual meeting recordings as well as between various settings of the overlap detection operation point. However, a high performance variability across different recordings is also typical of the baseline diarization system, without any overlap handling