6,254 research outputs found
Singing voice separation: a study on training data
In the recent years, singing voice separation systems showed increased
performance due to the use of supervised training. The design of training
datasets is known as a crucial factor in the performance of such systems. We
investigate on how the characteristics of the training dataset impacts the
separation performances of state-of-the-art singing voice separation
algorithms. We show that the separation quality and diversity are two important
and complementary assets of a good training dataset. We also provide insights
on possible transforms to perform data augmentation for this task
BaNa: a noise resilient fundamental frequency detection algorithm for speech and music
Fundamental frequency (F0) is one of the essential features in many acoustic related applications. Although numerous F0 detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F0 value among several F0 candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F0 detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.Peer ReviewedPostprint (author's final draft
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios
The CHiME challenges have played a significant role in the development and
evaluation of robust automatic speech recognition (ASR) systems. We introduce
the CHiME-7 distant ASR (DASR) task, within the 7th CHiME challenge. This task
comprises joint ASR and diarization in far-field settings with multiple, and
possibly heterogeneous, recording devices. Different from previous challenges,
we evaluate systems on 3 diverse scenarios: CHiME-6, DiPCo, and Mixer 6. The
goal is for participants to devise a single system that can generalize across
different array geometries and use cases with no a-priori information. Another
departure from earlier CHiME iterations is that participants are allowed to use
open-source pre-trained models and datasets. In this paper, we describe the
challenge design, motivation, and fundamental research questions in detail. We
also present the baseline system, which is fully array-topology agnostic and
features multi-channel diarization, channel selection, guided source separation
and a robust ASR model that leverages self-supervised speech representations
(SSLR)
The third 'CHiME' speech separation and recognition challenge: Analysis and outcomes
This paper presents the design and outcomes of the CHiME-3 challenge, the first open speech recognition evaluation designed to target the increasingly relevant multichannel, mobile-device speech recognition scenario. The paper serves two purposes. First, it provides a definitive reference for the challenge, including full descriptions of the task design, data capture and baseline systems along with a description and evaluation of the 26 systems that were submitted. The best systems re-engineered every stage of the baseline resulting in reductions in word error rate from 33.4% to as low as 5.8%. By comparing across systems, techniques that are essential for strong performance are identified. Second, the paper considers the problem of drawing conclusions from evaluations that use speech directly recorded in noisy environments. The degree of challenge presented by the resulting material is hard to control and hard to fully characterise. We attempt to dissect the various 'axes of difficulty' by correlating various estimated signal properties with typical system performance on a per session and per utterance basis. We find strong evidence of a dependence on signal-to-noise ratio and channel quality. Systems are less sensitive to variations in the degree of speaker motion. The paper concludes by discussing the outcomes of CHiME-3 in relation to the design of future mobile speech recognition evaluations
Multichannel Automatic Recognition of Voice Command in a Multi-Room Smart Home : an Experiment involving Seniors and Users with Visual Impairment
International audienceVoice command system in multi-room smart homes for assist- ing people in loss of autonomy in their daily activities must face several challenges, one of which being the distant condi- tion which impacts the ASR system performance. This paper presents an approach to improve voice command recognition at the decoding level by using multiple sources and model adap- tation. The method has been tested on data recorded with 11 elderly and visually impaired participants in a real smart home. The results show an error rate of 3.2% in off-line condition and of 13.2% in on-line condition
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
FEARLESS STEPS Challenge (FS-2): Supervised Learning with Massive Naturalistic Apollo Data
The Fearless Steps Initiative by UTDallas-CRSS led to the digitization,
recovery, and diarization of 19,000 hours of original analog audio data, as
well as the development of algorithms to extract meaningful information from
this multi-channel naturalistic data resource. The 2020 FEARLESS STEPS (FS-2)
Challenge is the second annual challenge held for the Speech and Language
Technology community to motivate supervised learning algorithm development for
multi-party and multi-stream naturalistic audio. In this paper, we present an
overview of the challenge sub-tasks, data, performance metrics, and lessons
learned from Phase-2 of the Fearless Steps Challenge (FS-2). We present
advancements made in FS-2 through extensive community outreach and feedback. We
describe innovations in the challenge corpus development, and present revised
baseline results. We finally discuss the challenge outcome and general trends
in system development across both phases (Phase FS-1 Unsupervised, and Phase
FS-2 Supervised) of the challenge, and its continuation into multi-channel
challenge tasks for the upcoming Fearless Steps Challenge Phase-3.Comment: Paper Accepted in the Interspeech 2020 Conferenc
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