Contains fulltext : 134621.pdf (Author’s version preprint ) (Open Access)

The speaker diarization system developed at the International Computer Science Institute (ICSI) has played a prominent role in the speaker diarization community, and many researchers in the rich transcription community have adopted methods and techniques developed for the ICSI speaker diarization engine. Although there have been many related publications over the years, previous articles only presented changes and improvements rather than a description of the full system. Attempting to replicate the ICSI speaker diarization system as a complete entity would require an extensive literature review, and might ultimately fail due to component description version mismatches. This paper therefore presents the first full conceptual description of the ICSI speaker diarization system as presented to the National Institute of Standards Technology Rich Transcription 2009 (NIST RT-09) evaluation, which consists of online and offline subsystems, multi-stream and single-stream implementations, and audio and audio-visual approaches. Some of the components, such as the online system, have not been previously described. The paper also includes all necessary preprocessing steps, such as Wiener filtering, speech activity detection and beamforming.

In this paper, we give an overview of the AMIDA systems for transcription of conference and lecture room meetings. The systems were developed for participation in the Rich Transcription evaluations conducted by the National Institute for Standards and Technology in the years 2007 and 2009 and can process close talking and far field microphone recordings. The paper first discusses fundamental properties of meeting data with special focus on the AMI/AMIDA corpora. This is followed by a description and analysis of improved processing and modeling, with focus on techniques specifically addressing meeting transcription issues such as multi-room recordings or domain variability. In 2007 and 2009, two different strategies of systems building were followed. While in 2007 we used our traditional style system design based on cross adaptation, the 2009 systems were constructed semi-automatically, supported by improved decoders and a new method for system representation. Overall these changes gave a 6%-13% relative reduction in word error rate compared to our 2007 results while at the same time requiring less training material and reducing the real-time factor by five times. The meeting transcription systems are available at www.webasr.org.

Performing speaker diarization of very long recordings is a problem for most diarization systems that are based on agglomerative clustering with an hidden Markov model (HMM) topology. Performing collection-wide speaker diarization, where each speaker is identified uniquely across the entire collection, is even a more challenging task. In this paper we propose a method with which it is possible to efficiently perform diarization of long recordings. We have also applied this method successfully to a collection of a total duration of approximately 15 hours. The method consists of first segmenting long recordings into smaller chunks on which diarization is performed. Next, a speaker detection system is used to link the speech clusters from each chunk and to assign a unique label to each speaker in the long recording or in the small collection. We show for three different audio collections that it is possible to perform high-quality diarization with this approach. The long meetings from the ICSI corpus are processed 5.5 times faster than the originally needed time and by uniquely labeling each speaker across the entire collection it becomes possible to perform speaker-based information retrieval with high accuracy (mean average precision of 0.57).

In this paper, we describe an analysis of our speaker diarization system based on a series of oracle experiments. In this analysis, each system component is substituted by an oracle component that uses the reference transcripts to perform flawlessly. By placing the original components back into the system one at a time, either in a top-down or bottom-up manner, the performance of each individual system component is measured. The analysis approach can be applied to any speaker diarization system that consists of a concatenation of separate components. Our experimental findings are relevant for most RT09s diarization systems that all apply similar techniques. The analysis revealed that three components caused most errors: speech activity detection, the inability to handle overlapping speech, and robustness of the merging component to cluster impurity.

In this paper we present a method for automatically generating acoustic sub-word units that can substitute conventional phone models in a query-by-example spoken term detection system. We generate the sub-word units with a modified version of our speaker diarization system. Given a speech recording, the original diarization system generates a set of speaker models in an unsupervised manner without the need for training or development data. Modifying the diarization system to process the speech of a single speaker and decreasing the minimum segment duration constraint allows us to detect speaker-dependent sub-word units. For the task of query-by-example spoken term detection, we show that the pro posed system performs well on both broadcast and non-broadcast recordings, unlike a conventional phone-based system trained solely on broadcast data. A mean average precision of 0.28 and 0.38 was obtained for experiments on broadcast news and on a set of war veteran interviews, respectively.

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If English is used intensively as a lingua franca in a multilanguage community, do speakers then converge towards a single common accent? This speech corpus allows for longitudinal study to investigate the question of convergence by means of repeated speech recordings of students at an English-language college over a period of 5 years. We describe the content and collection of the corpus and the type of research that is envisaged, as well as tools used to manage and analyze the recordings, including automatic phone recognition for prosodic analyses; and intelligibility experiments using the SRT method.

We present the AMIDA 2009 system for participation in the NIST RT’2009 STT evaluations. Systems for close-talking, far field and speaker attributed STT conditions are described. Im- provements to our previous systems are: segmentation and diar- isation; stacked bottle-neck posterior feature extraction; fMPE training of acoustic models; adaptation on complete meetings; improvements to WFST decoding; automatic optimisation of decoders and system graphs. Overall these changes gave a 6- 13% relative reduction in word error rate while at the same time reducing the real-time factor by a factor of five and using con- siderably less data for acoustic model training.

In this paper we present a method for combining multiple diarization systems into one single system by applying a majority voting scheme. The voting scheme selects the best segmentation purely on basis of the output of each system. On our development set of NIST Rich Transcription evaluation meetings the voting method improves our system on all evaluation conditions. For the single distant microphone condition, DER performance improved by 7:8% (relative) compared to the best input system. For the multiple distant microphone condition the improvement is 3:6%. Index Terms: Speaker diarization

In this paper we present the two-pass speaker diarization system that we developed for the NIST RT09s evaluation. In the first pass of our system a model for speech overlap detection is generated automatically. This model is used in two ways to reduce the diarization errors due to overlapping speech. First, it is used in a second diarization pass to remove overlapping speech from the data while training the speaker models. Second, it is used to find speech overlap for the final segmentation so that overlapping speech segments can be generated. The experiments show that our overlap detection method improves the performance ofall three of our system configurations. Index Terms: Speaker diarization, speech overlap detection, Benchmark

Decoders that make use of token-passing restrict their search space by various types of token pruning. With use of the Language Model Look-Ahead (LMLA) technique it is possible to increase the number of tokens that can be pruned without loss of decoding precision. Unfortunately, for token passing decoders that use single static pronunciation prefix trees, full n-gram LMLA increases the needed number of language model probability calculations considerably. In this paper a method for applying full n-gram LMLA in a decoder with a single static pronunciation tree is introduced. The experiments show that this method improves the speed of the decoder without an increase of search errors.

In this paper we discuss the performance analysis of a speaker diarization system similar to the system that was submitted by ICSI at the NIST RT06s evaluation benchmark. The analysis that is based on a series of oracle experiments, provides a good understanding of the performance of each system component on a test set of twelve conference meetings used in previous NIST benchmarks. Our analysis shows that the speech activity detection component contributes most to the total diarization error rate (23%). The lack of ability to model overlapping speech is also a large source of errors (22%) followed by the component that creates the initial system models (15%). Index Terms: speaker diarization, rich transcription 1.

In this paper we discuss the speech activity detection system that we used for detecting speech regions in the Dutch TRECVID video collection. The system is designed to filter non-speech like music or sound effects out of the signal without the use of predefined non-speech models. Because the system trains its models on-line, it is robust for handling out-ofdomain data. The speech activity error rate on an out-of-domain test set, recordings of English conference meetings, was 4.4%. The overall error rate on twelve randomly selected five minute TRECVID fragments was 11.5%. Index Terms: speech activity detection