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Speaker diarisation and longitudinal linking in multi-genre broadcast data
This paper presents a multi-stage speaker diarisation system with longitudinal linking developed on BBC multi-genre data for the 2015 Multi-Genre Broadcast (MGB) challenge. The basic speaker diarisation system draws on techniques from the Cambridge March 2005 system with a new deep neural network (DNN)-based speech/non speech segmenter. A newly developed linking stage is next added to the basic diarisation output aiming at the identification of speakers across multiple episodes of the same series. The longitudinal constraint imposes an incremental processing of the episodes, where speaker labels for each episode can be obtained using only material from the episode in question, and those broadcast earlier in time. The nature of the data as well as the longitudinal linking constraint position this diarisation task as a new open-research topic, and a particularly challenging one. Different linking clustering metrics are compared and the lowest within-episode and cross-episode DER scores are achieved on the MGB challenge evaluation set.This work is in part supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology). C. Zhang is also supported by a Cambridge International Scholarship from the Cambridge Commonwealth, European & International Trust.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ASRU.2015.740485
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
QCompere @ REPERE 2013
International audienceWe describe QCompere consortium submissions to the REPERE 2013 evaluation campaign. The REPERE challenge aims at gathering four communities (face recognition, speaker identification, optical character recognition and named entity detection) towards the same goal: multimodal person recognition in TV broadcast. First, four mono-modal components are introduced (one for each foregoing community) constituting the elementary building blocks of our various submissions. Then, depending on the target modality (speaker or face recognition) and on the task (supervised or unsupervised recognition), four different fusion techniques are introduced: they can be summarized as propagation-, classifier-, rule- or graph-based approaches. Finally, their performance is evaluated on REPERE 2013 test set and their advantages and limitations are discussed
A speaker rediarization scheme for improving diarization in large two-speaker telephone datasets
In this paper we propose a novel scheme for carrying out speaker diarization in an iterative manner. We aim to show that the information obtained through the first pass of speaker diarization can be reused to refine and improve the original diarization results. We call this technique speaker rediarization and demonstrate the practical application of our rediarization algorithm using a large archive of two-speaker telephone conversation recordings. We use the NIST 2008 SRE summed telephone corpora for evaluating our speaker rediarization system. This corpus contains recurring speaker identities across independent recording sessions that need to be linked across the entire corpus. We show that our speaker rediarization scheme can take advantage of inter-session speaker information, linked in the initial diarization pass, to achieve a 30% relative improvement over the original diarization error rate (DER) after only two iterations of rediarization
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
Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors
This work proposes a frame-wise online/streaming end-to-end neural
diarization (FS-EEND) method in a frame-in-frame-out fashion. To frame-wisely
detect a flexible number of speakers and extract/update their corresponding
attractors, we propose to leverage a causal speaker embedding encoder and an
online non-autoregressive self-attention-based attractor decoder. A look-ahead
mechanism is adopted to allow leveraging some future frames for effectively
detecting new speakers in real time and adaptively updating speaker attractors.
The proposed method processes the audio stream frame by frame, and has a low
inference latency caused by the look-ahead frames. Experiments show that,
compared with the recently proposed block-wise online methods, our method
FS-EEND achieves state-of-the-art diarization results, with a low inference
latency and computational cost
Leveraging Speech Separation for Conversational Telephone Speaker Diarization
Speech separation and speaker diarization have strong similarities. In
particular with respect to end-to-end neural diarization (EEND) methods.
Separation aims at extracting each speaker from overlapped speech, while
diarization identifies time boundaries of speech segments produced by the same
speaker. In this paper, we carry out an analysis of the use of speech
separation guided diarization (SSGD) where diarization is performed simply by
separating the speakers signals and applying voice activity detection. In
particular we compare two speech separation (SSep) models, both in offline and
online settings. In the online setting we consider both the use of continuous
source separation (CSS) and causal SSep models architectures. As an additional
contribution, we show a simple post-processing algorithm which reduces
significantly the false alarm errors of a SSGD pipeline. We perform our
experiments on Fisher Corpus Part 1 and CALLHOME datasets evaluating both
separation and diarization metrics. Notably, without fine-tuning, our SSGD
DPRNN-based online model achieves 12.7% DER on CALLHOME, comparable with
state-of-the-art EEND models despite having considerably lower latency, i.e.,
50 ms vs 1 s.Comment: Submitted to INTERSPEECH 202
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