2,039 research outputs found
Deep clustering: Discriminative embeddings for segmentation and separation
We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network approaches provide great advantages in terms of learning power and
speed, but previously it has been unclear how to use them to separate signals
in a class-independent way. In contrast, spectral clustering approaches are
flexible with respect to the classes and number of items to be segmented, but
it has been unclear how to leverage the learning power and speed of deep
networks. To obtain the best of both worlds, we use an objective function that
to train embeddings that yield a low-rank approximation to an ideal pairwise
affinity matrix, in a class-independent way. This avoids the high cost of
spectral factorization and instead produces compact clusters that are amenable
to simple clustering methods. The segmentations are therefore implicitly
encoded in the embeddings, and can be "decoded" by clustering. Preliminary
experiments show that the proposed method can separate speech: when trained on
spectrogram features containing mixtures of two speakers, and tested on
mixtures of a held-out set of speakers, it can infer masking functions that
improve signal quality by around 6dB. We show that the model can generalize to
three-speaker mixtures despite training only on two-speaker mixtures. The
framework can be used without class labels, and therefore has the potential to
be trained on a diverse set of sound types, and to generalize to novel sources.
We hope that future work will lead to segmentation of arbitrary sounds, with
extensions to microphone array methods as well as image segmentation and other
domains.Comment: Originally submitted on June 5, 201
A Novel Method For Speech Segmentation Based On Speakers' Characteristics
Speech Segmentation is the process change point detection for partitioning an
input audio stream into regions each of which corresponds to only one audio
source or one speaker. One application of this system is in Speaker Diarization
systems. There are several methods for speaker segmentation; however, most of
the Speaker Diarization Systems use BIC-based Segmentation methods. The main
goal of this paper is to propose a new method for speaker segmentation with
higher speed than the current methods - e.g. BIC - and acceptable accuracy. Our
proposed method is based on the pitch frequency of the speech. The accuracy of
this method is similar to the accuracy of common speaker segmentation methods.
However, its computation cost is much less than theirs. We show that our method
is about 2.4 times faster than the BIC-based method, while the average accuracy
of pitch-based method is slightly higher than that of the BIC-based method.Comment: 14 pages, 8 figure
Speaker tracking system using speaker boundary detection
This thesis is about a research conducted in the area of Speaker Recognition. The application is concerned to the automatic detection and tracking of target speakers in meetings, conferences, telephone conversations and in radio and television broadcasts. A Speaker Tracking system is developed here, in collaboration with the Center for Language and Speech Technologies and Applications (TALP) in UPC. The main objective of this Speaker Tracking system is to answer the question: When the target speaker speaks? The system uses training speech data for the target speaker in the pre-enrollment stage. Three main modules have been designed for this Speaker Tracking system. In the first module an energy based Speech Activity Detection is applied to select the speech parts of the audio. In the second module the audio is segmented according to the speaker turning points. In the last module a Speaker Verification is implemented in which the target speakers are verified and tracked. Two different approaches are applied in this last module. In the first approach for Speaker Verification, the target speakers and the segments are modeled using the state-of-the-art, Gaussian Mixture Models (GMM). In the second approach for Speaker Verification, the identity vectors (i-vectors) representation is applied for the target speakers and the segments. Finally, the performance of both these approaches is compared for the results evaluation
Automated speech and audio analysis for semantic access to multimedia
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
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
Development of a Speaker Diarization System for Speaker Tracking in Audio Broadcast News: a Case Study
A system for speaker tracking in broadcast-news audio data is presented and the impacts of the main components of the system to the overall speaker-tracking performance are evaluated. The process of speaker tracking in continuous audio streams
involves several processing tasks and is therefore treated as a multistage process. The main building blocks of such system include the components for audio segmentation, speech detection, speaker clustering and speaker identification. The aim of the first three processes is to find homogeneous regions in continuous audio streams that belong to one speaker and to join each region of the same speaker together. The task of organizing the audio data in this way is known as speaker diarization and plays an important role in various speech-processing applications.
In our case the impact of speaker diarization
was assessed in a speaker-tracking system by performing a comparative study of how each of the component influenced the overall speaker-detection results. The evaluation experiments were performed on broadcast-news audio data with a speaker-tracking system,
which was capable of detecting 41 target speakers. We implemented several different approaches in each component of the system and compared their performances by inspecting the final speaker-tracking results. The evaluation results indicate the importance of the audio-segmentation and speech-detection components, while no significant improvement of the overall results was achieved by additionally including a speaker-clustering component to the speaker-tracking system
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
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