25,863 research outputs found
Segmentation, Diarization and Speech Transcription: Surprise Data Unraveled
In this thesis, research on large vocabulary continuous speech recognition for unknown audio conditions is presented. For automatic speech recognition systems based on statistical methods, it is important that the conditions of the audio used for training the statistical models match the conditions of the audio to be processed. Any mismatch will decrease the accuracy of the recognition. If it is unpredictable what kind of data can be expected, or in other words if the conditions of the audio to be processed are unknown, it is impossible to tune the models. If the material consists of `surprise data' the output of the system is likely to be poor. In this thesis methods are presented for which no external training data is required for training models. These novel methods have been implemented in a large vocabulary continuous speech recognition system called SHoUT. This system consists of three subsystems: speech/non-speech classification, speaker diarization and automatic speech recognition. The speech/non-speech classification subsystem separates speech from silence and unknown audible non-speech events. The type of non-speech present in audio recordings can vary from paper shuffling in recordings of meetings to sound effects in television shows. Because it is unknown what type of non-speech needs to be detected, it is not possible to train high quality statistical models for each type of non-speech sound. The speech/non-speech classification subsystem, also called the speech activity detection subsystem, does not attempt to classify all audible non-speech in a single run. Instead, first a bootstrap speech/silence classification is obtained using a standard speech activity component. Next, the models for speech, silence and audible non-speech are trained on the target audio using the bootstrap classification. This approach makes it possible to classify speech and non-speech with high accuracy, without the need to know what kinds of sound are present in the audio recording. Once all non-speech is filtered out of the audio, it is the task of the speaker diarization subsystem to determine how many speakers occur in the recording and exactly when they are speaking. The speaker diarization subsystem applies agglomerative clustering to create clusters of speech fragments for each speaker in the recording. First, statistical speaker models are created on random chunks of the recording and by iteratively realigning the data, retraining the models and merging models that represent the same speaker, accurate speaker models are obtained for speaker clustering. This method does not require any statistical models developed on a training set, which makes the diarization subsystem insensitive for variation in audio conditions. Unfortunately, because the algorithm is of complexity , this clustering method is slow for long recordings. Two variations of the subsystem are presented that reduce the needed computational effort, so that the subsystem is applicable for long audio recordings as well. The automatic speech recognition subsystem developed for this research, is based on Viterbi decoding on a fixed pronunciation prefix tree. Using the fixed tree, a flexible modular decoder could be developed, but it was not straightforward to apply full language model look-ahead efficiently. In this thesis a novel method is discussed that makes it possible to apply language model look-ahead effectively on the fixed tree. Also, to obtain higher speech recognition accuracy on audio with unknown acoustical conditions, a selection from the numerous known methods that exist for robust automatic speech recognition is applied and evaluated in this thesis. The three individual subsystems as well as the entire system have been successfully evaluated on three international benchmarks. The diarization subsystem has been evaluated at the NIST RT06s benchmark and the speech activity detection subsystem has been tested at RT07s. The entire system was evaluated at N-Best, the first automatic speech recognition benchmark for Dutch
Towards Affordable Disclosure of Spoken Word Archives
This paper presents and discusses ongoing work aiming at affordable disclosure of real-world spoken word archives in general, and in particular of a collection of recorded interviews with Dutch survivors of World War II concentration camp Buchenwald. Given such collections, the least we want to be able to provide is search at different levels and a flexible way of presenting results. Strategies for automatic annotation based on speech recognition ā supporting e.g., within-document searchā are outlined and discussed with respect to the Buchenwald interview collection. In addition, usability aspects of the spoken word search are discussed on the basis of our experiences with the online Buchenwald web portal. It is concluded that, although user feedback is generally fairly positive, automatic annotation performance is still far from satisfactory, and requires additional research
Detection is the central problem in real-word spelling correction
Real-word spelling correction differs from non-word spelling correction in
its aims and its challenges. Here we show that the central problem in real-word
spelling correction is detection. Methods from non-word spelling correction,
which focus instead on selection among candidate corrections, do not address
detection adequately, because detection is either assumed in advance or heavily
constrained. As we demonstrate in this paper, merely discriminating between the
intended word and a random close variation of it within the context of a
sentence is a task that can be performed with high accuracy using
straightforward models. Trigram models are sufficient in almost all cases. The
difficulty comes when every word in the sentence is a potential error, with a
large set of possible candidate corrections. Despite their strengths, trigram
models cannot reliably find true errors without introducing many more, at least
not when used in the obvious sequential way without added structure. The
detection task exposes weakness not visible in the selection task
Statistical assessment of speech system performance
Methods for the normalization of performance tests results of speech recognition systems are presented. Technological accomplishments in speech recognition systems, as well as planned research activities are described
Automated speech and audio analysis for semantic access to multimedia
The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives
Semantic Processing of Out-Of-Vocabulary Words in a Spoken Dialogue System
One of the most important causes of failure in spoken dialogue systems is
usually neglected: the problem of words that are not covered by the system's
vocabulary (out-of-vocabulary or OOV words). In this paper a methodology is
described for the detection, classification and processing of OOV words in an
automatic train timetable information system. The various extensions that had
to be effected on the different modules of the system are reported, resulting
in the design of appropriate dialogue strategies, as are encouraging evaluation
results on the new versions of the word recogniser and the linguistic
processor.Comment: 4 pages, 2 eps figures, requires LaTeX2e, uses eurospeech.sty and
epsfi
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