30 research outputs found
The use of long-term features for GMM- and i-vector-based speaker diarization systems
Several factors contribute to the performance of speaker diarization systems. For instance, the appropriate selection of speech features is one of the key aspects that affect speaker diarization systems. The other factors include the techniques employed to perform both segmentation and clustering. While the static mel frequency cepstral coefficients are the most widely used features in speech-related tasks including speaker diarization, several studies have shown the benefits of augmenting regular speech features with the static ones.
In this work, we have proposed and assessed the use of voice-quality features (i.e., jitter, shimmer, and Glottal-to-Noise Excitation ratio) within the framework of speaker diarization. These acoustic attributes are employed together with the state-of-the-art short-term cepstral and long-term prosodic features. Additionally, the use of delta dynamic features is also explored separately both for segmentation and bottom-up clustering sub-tasks. The combination of the different feature sets is carried out at several levels. At the feature level, the long-term speech features are stacked in the same feature vector. At the score level, the short- and long-term speech features are independently modeled and fused at the score likelihood level.
Various feature combinations have been applied both for Gaussian mixture modeling and i-vector-based speaker diarization systems. The experiments have been carried out on Augmented Multi-party Interaction meeting corpus. The best result, in terms of diarization error rate, is reported by using i-vector-based cosine-distance clustering together with a signal parameterization consisting of a combination of static cepstral coefficients, delta, voice-quality, and prosodic features. The best result shows about 24% relative diarization error rate improvement compared to the baseline system which is based on Gaussian mixture modeling and short-term static cepstral coefficients.Peer ReviewedPostprint (published version
Spot the conversation: speaker diarisation in the wild
The goal of this paper is speaker diarisation of videos collected 'in the
wild'. We make three key contributions. First, we propose an automatic
audio-visual diarisation method for YouTube videos. Our method consists of
active speaker detection using audio-visual methods and speaker verification
using self-enrolled speaker models. Second, we integrate our method into a
semi-automatic dataset creation pipeline which significantly reduces the number
of hours required to annotate videos with diarisation labels. Finally, we use
this pipeline to create a large-scale diarisation dataset called VoxConverse,
collected from 'in the wild' videos, which we will release publicly to the
research community. Our dataset consists of overlapping speech, a large and
diverse speaker pool, and challenging background conditions.Comment: The dataset will be available for download from
http://www.robots.ox.ac.uk/~vgg/data/voxceleb/voxconverse.html . The
development set will be released in July 2020, and the test set will be
released in October 202
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
Adaptation of speech recognition systems to selected real-world deployment conditions
Tato habilitační práce se zabývá problematikou adaptace systémů
rozpoznávání řeči na vybrané reálné podmínky nasazení. Je koncipována
jako sborník celkem dvanácti článků, které se touto problematikou
zabývají. Jde o publikace, jejichž jsem hlavním autorem
nebo spoluatorem, a které vznikly v rámci několika navazujících
výzkumných projektů. Na řešení těchto projektů jsem se
podílel jak v roli člena výzkumného týmu, tak i v roli řešitele nebo
spoluřešitele.
Publikace zařazené do tohoto sborníku lze rozdělit podle tématu
do tří hlavních skupin. Jejich společným jmenovatelem je
snaha přizpůsobit daný rozpoznávací systém novým podmínkám či
konkrétnímu faktoru, který významným způsobem ovlivňuje jeho
funkci či přesnost.
První skupina článků se zabývá úlohou neřízené adaptace na
mluvčího, kdy systém přizpůsobuje svoje parametry specifickým
hlasovým charakteristikám dané mluvící osoby. Druhá část práce
se pak věnuje problematice identifikace neřečových událostí na vstupu
do systému a související úloze rozpoznávání řeči s hlukem
(a zejména hudbou) na pozadí. Konečně třetí část práce se zabývá
přístupy, které umožňují přepis audio signálu obsahujícího promluvy
ve více než v jednom jazyce. Jde o metody adaptace existujícího
rozpoznávacího systému na nový jazyk a metody identifikace
jazyka z audio signálu.
Obě zmíněné identifikační úlohy jsou přitom vyšetřovány zejména
v náročném a méně probádaném režimu zpracování po jednotlivých
rámcích vstupního signálu, který je jako jediný vhodný pro on-line
nasazení, např. pro streamovaná data.This habilitation thesis deals with adaptation of automatic speech
recognition (ASR) systems to selected real-world deployment conditions.
It is presented in the form of a collection of twelve articles
dealing with this task; I am the main author or a co-author of these
articles. They were published during my work on several consecutive
research projects. I have participated in the solution of them
as a member of the research team as well as the investigator or a
co-investigator.
These articles can be divided into three main groups according to
their topics. They have in common the effort to adapt a particular
ASR system to a specific factor or deployment condition that affects
its function or accuracy.
The first group of articles is focused on an unsupervised speaker
adaptation task, where the ASR system adapts its parameters to
the specific voice characteristics of one particular speaker. The second
part deals with a) methods allowing the system to identify
non-speech events on the input, and b) the related task of recognition
of speech with non-speech events, particularly music, in the
background. Finally, the third part is devoted to the methods
that allow the transcription of an audio signal containing multilingual
utterances. It includes a) approaches for adapting the existing
recognition system to a new language and b) methods for identification
of the language from the audio signal.
The two mentioned identification tasks are in particular investigated
under the demanding and less explored frame-wise scenario,
which is the only one suitable for processing of on-line data streams