9,730 research outputs found
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction,
topic detection, or browsing/playback is to segment the input into sentence and
topic units. Speech segmentation is challenging, since the cues typically
present for segmenting text (headers, paragraphs, punctuation) are absent in
spoken language. We investigate the use of prosody (information gleaned from
the timing and melody of speech) for these tasks. Using decision tree and
hidden Markov modeling techniques, we combine prosodic cues with word-based
approaches, and evaluate performance on two speech corpora, Broadcast News and
Switchboard. Results show that the prosodic model alone performs on par with,
or better than, word-based statistical language models -- for both true and
automatically recognized words in news speech. The prosodic model achieves
comparable performance with significantly less training data, and requires no
hand-labeling of prosodic events. Across tasks and corpora, we obtain a
significant improvement over word-only models using a probabilistic combination
of prosodic and lexical information. Inspection reveals that the prosodic
models capture language-independent boundary indicators described in the
literature. Finally, cue usage is task and corpus dependent. For example, pause
and pitch features are highly informative for segmenting news speech, whereas
pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2),
Special Issue on Accessing Information in Spoken Audio, September 200
Blind Normalization of Speech From Different Channels
We show how to construct a channel-independent representation of speech that
has propagated through a noisy reverberant channel. This is done by blindly
rescaling the cepstral time series by a non-linear function, with the form of
this scale function being determined by previously encountered cepstra from
that channel. The rescaled form of the time series is an invariant property of
it in the following sense: it is unaffected if the time series is transformed
by any time-independent invertible distortion. Because a linear channel with
stationary noise and impulse response transforms cepstra in this way, the new
technique can be used to remove the channel dependence of a cepstral time
series. In experiments, the method achieved greater channel-independence than
cepstral mean normalization, and it was comparable to the combination of
cepstral mean normalization and spectral subtraction, despite the fact that no
measurements of channel noise or reverberations were required (unlike spectral
subtraction).Comment: 25 pages, 7 figure
Learning to detect dysarthria from raw speech
Speech classifiers of paralinguistic traits traditionally learn from diverse
hand-crafted low-level features, by selecting the relevant information for the
task at hand. We explore an alternative to this selection, by learning jointly
the classifier, and the feature extraction. Recent work on speech recognition
has shown improved performance over speech features by learning from the
waveform. We extend this approach to paralinguistic classification and propose
a neural network that can learn a filterbank, a normalization factor and a
compression power from the raw speech, jointly with the rest of the
architecture. We apply this model to dysarthria detection from sentence-level
audio recordings. Starting from a strong attention-based baseline on which
mel-filterbanks outperform standard low-level descriptors, we show that
learning the filters or the normalization and compression improves over fixed
features by 10% absolute accuracy. We also observe a gain over OpenSmile
features by learning jointly the feature extraction, the normalization, and the
compression factor with the architecture. This constitutes a first attempt at
learning jointly all these operations from raw audio for a speech
classification task.Comment: 5 pages, 3 figures, submitted to ICASS
Listeners normalize speech for contextual speech rate even without an explicit recognition task
Speech can be produced at different rates. Listeners take this rate variation into account by normalizing vowel duration for contextual speech rate: An ambiguous Dutch word /m?t/ is perceived as short /mAt/ when embedded in a slow context, but long /ma:t/ in a fast context. Whilst some have argued that this rate normalization involves low-level automatic perceptual processing, there is also evidence that it arises at higher-level cognitive processing stages, such as decision making. Prior research on rate-dependent speech perception has only used explicit recognition tasks to investigate the phenomenon, involving both perceptual processing and decision making. This study tested whether speech rate normalization can be observed without explicit decision making, using a cross-modal repetition priming paradigm. Results show that a fast precursor sentence makes an embedded ambiguous prime (/m?t/) sound (implicitly) more /a:/-like, facilitating lexical access to the long target word "maat" in a (explicit) lexical decision task. This result suggests that rate normalization is automatic, taking place even in the absence of an explicit recognition task. Thus, rate normalization is placed within the realm of everyday spoken conversation, where explicit categorization of ambiguous sounds is rare
Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition
In this paper, we investigate the use of invariant features for speaker recognition. Owing to their characteristics, these features are introduced to cope with the difficult and challenging problem of sensor variability and the source of performance degradation inherent in speaker recognition systems. Our experiments show: (1) the effectiveness of these features in match cases; (2) the benefit of combining these features with the mel frequency cepstral coefficients to exploit their discrimination power under uncontrolled conditions (mismatch cases). Consequently, the proposed invariant features result in a performance improvement as demonstrated by a reduction in the equal error rate and the minimum decision cost function compared to the GMM-UBM speaker recognition systems based on MFCC features
Hybrid Method for Digits Recognition using Fixed-Frame Scores and Derived Pitch
This paper presents a procedure of frame normalization based on the traditional dynamic time warping (DTW) using the LPC coefficients. The redefined method is called as the DTW frame-fixing method (DTW-FF), it works by normalizing the word frames of the input against the
reference frames. The enthusiasm to this study is due to neural network limitation that entails a fix number of input nodes for when processing multiple inputs in parallel. Due to this problem, this research is initiated to reduce the amount of computation and complexity in a neural network by reducing the number of inputs into the network. In this study, dynamic warping process is used, in which local distance scores of the warping path are fixed and collected so that their scores are of equal number of frames. Also studied in this paper is the
consideration of pitch as a contributing feature to the speech recognition. Results showed a good performance and
improvement when using pitch along with DTW-FF feature.
The convergence rate between using the steepest gradient
descent is also compared to another method namely conjugate
gradient method. Convergence rate is also improved when
conjugate gradient method is introduced in the back-propagation algorithm
- …