279,107 research outputs found
On the use of high-level information in speaker and language recognition
Actas de las IV Jornadas de TecnologĂa del Habla (JTH 2006)Automatic Speaker Recognition systems have been largely dominated by acoustic-spectral based systems, relying in proper modelling of the short-term vocal tract of speakers. However, there is scientific and intuitive evidence that speaker specific
information is embedded in the speech signal in multiple short- and long-term characteristics. In this work, a multilevel speaker recognition system combining acoustic, phonotactic and prosodic subsystems is presented and assessed using NIST 2005 Speaker Recognition Evaluation data.
For language recognition systems, the NIST 2005 Language Recognition Evaluation was selected to measure performance of a high-level language recognition systems
Convolutional Neural Network Architectures for Gender, Emotional Detection from Speech and Speaker Diarization
This paper introduces three system architectures for speaker identification that aim to overcome the limitations of diarization and voice-based biometric systems. Diarization systems utilize unsupervised algorithms to segment audio data based on the time boundaries of utterances, but they do not distinguish individual speakers. On the other hand, voice-based biometric systems can only identify individuals in recordings with a single speaker. Identifying speakers in recordings of natural conversations can be challenging, especially when emotional shifts can alter voice characteristics, making gender identification difficult. To address this issue, the proposed architectures include techniques for gender, emotion, and diarization at either the segment or group level. The evaluation of these architectures utilized two speech databases, namely VoxCeleb and RAVDESS (Ryerson audio-visual database of emotional speech and song) datasets. The findings reveal that the proposed approach outperforms the strategy level in terms of recognition results, despite the real-time processing advantage of the latter. The challenge of identifying multiple speakers engaging in a conversation while considering emotional changes that impact speech is effectively addressed by the proposed architectures. The data indicates that the gender and emotion classification of diarization achieves an accuracy of over 98 percent. These results suggest that the proposed speech-based approach can achieve highly accurate speaker identification
Automatic Quality Estimation for ASR System Combination
Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and other ASR decoder features. This information, which is
not always available, highly depends on the decoding process and sometimes
tends to over estimate the real quality of the recognized words. In this paper
we propose a novel variant of ROVER that takes advantage of ASR quality
estimation (QE) for ranking the transcriptions at "segment level" instead of:
i) relying on confidence scores, or ii) feeding ROVER with randomly ordered
hypotheses. We first introduce an effective set of features to compensate for
the absence of ASR decoder information. Then, we apply QE techniques to perform
accurate hypothesis ranking at segment-level before starting the fusion
process. The evaluation is carried out on two different tasks, in which we
respectively combine hypotheses coming from independent ASR systems and
multi-microphone recordings. In both tasks, it is assumed that the ASR decoder
information is not available. The proposed approach significantly outperforms
standard ROVER and it is competitive with two strong oracles that e xploit
prior knowledge about the real quality of the hypotheses to be combined.
Compared to standard ROVER, the abs olute WER improvements in the two
evaluation scenarios range from 0.5% to 7.3%
Semi-Supervised Speech Emotion Recognition with Ladder Networks
Speech emotion recognition (SER) systems find applications in various fields
such as healthcare, education, and security and defense. A major drawback of
these systems is their lack of generalization across different conditions. This
problem can be solved by training models on large amounts of labeled data from
the target domain, which is expensive and time-consuming. Another approach is
to increase the generalization of the models. An effective way to achieve this
goal is by regularizing the models through multitask learning (MTL), where
auxiliary tasks are learned along with the primary task. These methods often
require the use of labeled data which is computationally expensive to collect
for emotion recognition (gender, speaker identity, age or other emotional
descriptors). This study proposes the use of ladder networks for emotion
recognition, which utilizes an unsupervised auxiliary task. The primary task is
a regression problem to predict emotional attributes. The auxiliary task is the
reconstruction of intermediate feature representations using a denoising
autoencoder. This auxiliary task does not require labels so it is possible to
train the framework in a semi-supervised fashion with abundant unlabeled data
from the target domain. This study shows that the proposed approach creates a
powerful framework for SER, achieving superior performance than fully
supervised single-task learning (STL) and MTL baselines. The approach is
implemented with several acoustic features, showing that ladder networks
generalize significantly better in cross-corpus settings. Compared to the STL
baselines, the proposed approach achieves relative gains in concordance
correlation coefficient (CCC) between 3.0% and 3.5% for within corpus
evaluations, and between 16.1% and 74.1% for cross corpus evaluations,
highlighting the power of the architecture
The Microsoft 2017 Conversational Speech Recognition System
We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acoustic model to the set of model architectures we combined
previously, and includes character-based and dialog session aware LSTM language
models in rescoring. For system combination we adopt a two-stage approach,
whereby subsets of acoustic models are first combined at the senone/frame
level, followed by a word-level voting via confusion networks. We also added a
confusion network rescoring step after system combination. The resulting system
yields a 5.1\% word error rate on the 2000 Switchboard evaluation set
Spoken content retrieval: A survey of techniques and technologies
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR
Deep factorization for speech signal
Various informative factors mixed in speech signals, leading to great
difficulty when decoding any of the factors. An intuitive idea is to factorize
each speech frame into individual informative factors, though it turns out to
be highly difficult. Recently, we found that speaker traits, which were assumed
to be long-term distributional properties, are actually short-time patterns,
and can be learned by a carefully designed deep neural network (DNN). This
discovery motivated a cascade deep factorization (CDF) framework that will be
presented in this paper. The proposed framework infers speech factors in a
sequential way, where factors previously inferred are used as conditional
variables when inferring other factors. We will show that this approach can
effectively factorize speech signals, and using these factors, the original
speech spectrum can be recovered with a high accuracy. This factorization and
reconstruction approach provides potential values for many speech processing
tasks, e.g., speaker recognition and emotion recognition, as will be
demonstrated in the paper.Comment: Accepted by ICASSP 2018. arXiv admin note: substantial text overlap
with arXiv:1706.0177
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