227 research outputs found
Cross-lingual dysphonic speech detection using pretrained speaker embeddings
In this study, cross-lingual binary classification and severity estimation of dysphonic speech have been carried out. Hand-crafted acoustic feature extraction is replaced by the speaker embedding techniques used in the speaker verification. Two state of art deep learning methods for speaker verification have been used: the X-vector and ECAPA-TDNN. Embeddings are extracted from speech samples in Hungarian and Dutch languages and used to train Support Vector Machine (SVM) and Support Vector Regressor (SVR) for binary classification and severity estimation, in a cross-language manner. Our results were competitive with manual feature engineering, when the models were trained on Hungarian samples and evaluated on Dutch samples in the binary classification of dysphonic speech and outperformed in estimating the severity level of dysphonic speech. Moreover, our model achieved 0.769 and 0.771 in Spearman and Pearson correlations. Also, our results in both classification and regression were superior compared to manual feature extraction technique when models were trained on Dutch samples and evaluated on Hungarian samples with only a limited number of samples are available for training. An accuracy of 86.8% was reached with features extracted from embedding methods, while the maximum accuracy using hand-crafted acoustic features was 66.8%. Overall results show that Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN) performs better than the former X-vector in both tasks
Effects of language mismatch in automatic forensic voice comparison using deep learning embeddings
In forensic voice comparison the speaker embedding has become widely popular
in the last 10 years. Most of the pretrained speaker embeddings are trained on
English corpora, because it is easily accessible. Thus, language dependency can
be an important factor in automatic forensic voice comparison, especially when
the target language is linguistically very different. There are numerous
commercial systems available, but their models are mainly trained on a
different language (mostly English) than the target language. In the case of a
low-resource language, developing a corpus for forensic purposes containing
enough speakers to train deep learning models is costly. This study aims to
investigate whether a model pre-trained on English corpus can be used on a
target low-resource language (here, Hungarian), different from the model is
trained on. Also, often multiple samples are not available from the offender
(unknown speaker). Therefore, samples are compared pairwise with and without
speaker enrollment for suspect (known) speakers. Two corpora are applied that
were developed especially for forensic purposes, and a third that is meant for
traditional speaker verification. Two deep learning based speaker embedding
vector extraction methods are used: the x-vector and ECAPA-TDNN. Speaker
verification was evaluated in the likelihood-ratio framework. A comparison is
made between the language combinations (modeling, LR calibration, evaluation).
The results were evaluated by minCllr and EER metrics. It was found that the
model pre-trained on a different language but on a corpus with a huge amount of
speakers performs well on samples with language mismatch. The effect of sample
durations and speaking styles were also examined. It was found that the longer
the duration of the sample in question the better the performance is. Also,
there is no real difference if various speaking styles are applied
Deep Spoken Keyword Spotting:An Overview
Spoken keyword spotting (KWS) deals with the identification of keywords in
audio streams and has become a fast-growing technology thanks to the paradigm
shift introduced by deep learning a few years ago. This has allowed the rapid
embedding of deep KWS in a myriad of small electronic devices with different
purposes like the activation of voice assistants. Prospects suggest a sustained
growth in terms of social use of this technology. Thus, it is not surprising
that deep KWS has become a hot research topic among speech scientists, who
constantly look for KWS performance improvement and computational complexity
reduction. This context motivates this paper, in which we conduct a literature
review into deep spoken KWS to assist practitioners and researchers who are
interested in this technology. Specifically, this overview has a comprehensive
nature by covering a thorough analysis of deep KWS systems (which includes
speech features, acoustic modeling and posterior handling), robustness methods,
applications, datasets, evaluation metrics, performance of deep KWS systems and
audio-visual KWS. The analysis performed in this paper allows us to identify a
number of directions for future research, including directions adopted from
automatic speech recognition research and directions that are unique to the
problem of spoken KWS
X-VECTORS: ROBUST NEURAL EMBEDDINGS FOR SPEAKER RECOGNITION
Speaker recognition is the task of identifying speakers based on their speech signal. Typically, this involves comparing speech from a known speaker, with recordings from unknown speakers, and making same-or-different speaker decisions. If the lexical contents of the recordings are fixed to some phrase, the task is considered text-dependent, otherwise it is text-independent. This dissertation is primarily concerned with this second, less constrained problem. Since speech data lives in a complex, high-dimensional space, it is difficult to directly compare speakers. Comparisons are facilitated by embeddings: mappings from complex input patterns to low-dimensional Euclidean spaces where notions of distance or similarity are defined in natural ways. For almost ten years, systems based on i-vectors--a type of embedding extracted from a traditional generative model--have been the dominant paradigm in this field. However, in other areas of applied machine learning, such as text or vision, embeddings extracted from discriminatively trained neural networks are the state-of-the-art. Recently, this line of research has become very active in speaker recognition as well. Neural networks are a natural choice for this purpose, as they are capable of learning extremely complex mappings, and when training data resources are abundant, tend to outperform traditional methods. In this dissertation, we develop a next-generation neural embedding--denoted by x-vector--for speaker recognition. These neural embeddings are demonstrated to substantially improve upon the state-of-the-art on a number of benchmark datasets
Analysis of the sensitivity of the End-Of-Turn Detection task to errors generated by the Automatic Speech Recognition process.
An End-Of-Turn Detection Module (EOTD-M) is an essential component of au- tomatic Spoken Dialogue Systems. The capability of correctly detecting whether a user’s utterance has ended or not improves the accuracy in interpreting the meaning of the message and decreases the latency in the answer. Usually, in di- alogue systems, an EOTD-M is coupled with an Automatic Speech Recognition Module (ASR-M) to transmit complete utterances to the Natural Language Un- derstanding unit. Mistakes in the ASR-M transcription can have a strong effect on the performance of the EOTD-M. The actual extent of this effect depends on the particular combination of ASR-M transcription errors and the sentence featurization techniques implemented as part of the EOTD-M. In this paper we investigate this important relationship for an EOTD-M based on semantic information and particular characteristics of the speakers (speech profiles). We introduce an Automatic Speech Recognition Simulator (ASR-SIM) that mod- els different types of semantic mistakes in the ASR-M transcription as well as different speech profiles. We use the simulator to evaluate the sensitivity to ASR-M mistakes of a Long Short-Term Memory network classifier trained in EOTD with different featurization techniques. Our experiments reveal the dif- ferent ways in which the performance of the model is influenced by the ASR-M errors. We corroborate that not only is the ASR-SIM useful to estimate the performance of an EOTD-M in customized noisy scenarios, but it can also be used to generate training datasets with the expected error rates of real working conditions, which leads to better performance.EMPATHIC
IT1244-19
TIN2016-78365-R
PID2019-104966GB-I00
Deep neural network techniques for monaural speech enhancement: state of the art analysis
Deep neural networks (DNN) techniques have become pervasive in domains such
as natural language processing and computer vision. They have achieved great
success in these domains in task such as machine translation and image
generation. Due to their success, these data driven techniques have been
applied in audio domain. More specifically, DNN models have been applied in
speech enhancement domain to achieve denosing, dereverberation and
multi-speaker separation in monaural speech enhancement. In this paper, we
review some dominant DNN techniques being employed to achieve speech
separation. The review looks at the whole pipeline of speech enhancement from
feature extraction, how DNN based tools are modelling both global and local
features of speech and model training (supervised and unsupervised). We also
review the use of speech-enhancement pre-trained models to boost speech
enhancement process. The review is geared towards covering the dominant trends
with regards to DNN application in speech enhancement in speech obtained via a
single speaker.Comment: conferenc
Deep representation learning for speech recognition
Representation learning is a fundamental ingredient of deep learning. However, learning a good representation is a challenging task. For speech recognition, such a representation should contain the information needed to perform well in this task. A robust representation should also be reusable, hence it should capture the structure of the data. Interpretability is another desired characteristic. In this thesis we strive to learn an optimal deep representation for speech recognition using feed-forward Neural Networks (NNs) with different connectivity patterns.
First and foremost, we aim to improve the robustness of the acoustic models. We use attribute-aware and adaptive training strategies to model the underlying factors of variation related to the speakers and the acoustic conditions. We focus on low-latency and real-time decoding scenarios. We explore different utterance summaries (referred to as utterance embeddings), capturing various sources of speech variability, and we seek to optimise speaker adaptive training (SAT) with control networks acting on the embeddings. We also propose a multi-scale CNN layer, to learn factorised representations. The proposed multi-scale approach also tackles the computational and memory efficiency.
We also present a number of different approaches as an attempt to better understand learned representations. First, with a controlled design, we aim to assess the role of individual components of deep CNN acoustic models. Next, with saliency maps, we evaluate the importance of each input feature with respect to the classification criterion. Then, we propose to evaluate layer-wise and model-wise learned representations in different diagnostic verification tasks (speaker and acoustic condition verification). We propose a deep CNN model as the embedding extractor, merging the information learned at different layers in the network. Similarly, we perform the analyses for the embeddings used in SAT-DNNs to gain more insight. For the multi-scale models, we also show how to compare learned representations (and assess their robustness) with a metric invariant to affine transformations
- …