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A review of available audio-visual speech corpora and a description of a new multimodal corpus of English speech recordings is provided. The new corpus containing 31 hours of recordings was created specifically to assist audio-visual speech recognition systems (AVSR) development. The database related to the corpus includes high-resolution, high-framerate stereoscopic video streams from RGB cameras, depth imaging stream utilizing Time-of-Flight camera accompanied by audio recorded using both: a microphone array and a microphone built in a mobile computer. For the purpose of applications related to AVSR systems training, every utterance was manually labeled, resulting in label files added to the corpus repository. Owing to the inclusion of recordings made in noisy conditions the elaborated corpus can also be used for testing robustness of speech recognition systems in the presence of acoustic background noise. The process of building the corpus, including the recording, labeling and post-processing phases is described in the paper. Results achieved with the developed audio-visual automatic speech recognition (ASR) engine trained and tested with the material contained in the corpus are presented and discussed together with comparative test results employing a state-of-the-art/commercial ASR engine. In order to demonstrate the practical use of the corpus it is made available for the public use.
The multimodal database presented in this paper aims to address above mentioned problems. It is distributed free of charge to any interested researcher. It is focused on high recording quality, ease of use and versatility. All videos were recorded in 1080p HD format, with 100 frames per second. To extend the number of potential fields of use of the dataset, several additional modalities were introduced. Consequently, researchers intending to incorporate facial depth information in their experiments can do that owing to the second camera applied to form a stereo pair with the first one or by utilizing the recordings from the Time-of-Flight camera. Investigating the influence of reverberation and noise on recognition results is also possible, because additional noise sources and a set of 8 microphones capturing sound at different distances from the speaker were used. Moreover, SNR (signal-to-noise ratio) values were calculated and made accessible for every uttered word (a detailed description of this functionality is to be found in Section 3.4).
The remainder of the paper is organized as follows: Section 2 provides a review of currently available audio-visual corpora. Our methods related to the corpus registration, including used language material, hardware setup and data processing steps are covered in Section 3, whereas Section 4 contains a description of the structure of the published database, together with the explanation of the procedure of gaining an access to it. Hitherto conceived use-cases of the database are also presented. Example speech recognition results achieved using our database, together with procedures and methods employed in experiments are discussed in Section 5. The paper concludes with some general remarks and observations in Section 6.
The available datasets suitable for AVSR research are relatively scarce, compared to the number of corpora containing audio material only. This results from the fact that the field of AVSR is still a developing relatively young research discipline. Another cause may be the multitude of requirements needed to be fulfilled in order to build a sizable audio-visual corpus, namely: a fully synchronized audio-visual stream, a large disk space, and a reliable method of data distribution (Durand et al. 2014).
As high-quality audio can be provided with relatively low costs, thus the main focus during the development of a AVSR corpus should be put on the visual data. Both: high resolution of video image and high framerate are needed in order to capture lip movement in space and time, accurately. The size of the speaker population depends on the declared purpose of the corpus - those focused on speech recognition, generally require employment of a smaller number of speakers than the ones intended for the use in speaker verification systems. The purpose of the corpus also affects the language material - continuous speech is favorable when testing speech recognition algorithms, while speaker verification can be done with separated words. Ideally, a corpus should contain both above types of speech. The following paragraphs discuss historic and modern audio-visual corpora in terms of: speaker population, language material, quality, and some other additional features. The described corpora contain English language material unless stated otherwise.
History of audio-visual datasets begins in 1984, when a first corpus was proposed by Petajan (1988) to support a lip reading digit recognizer. The first corpora were relatively low-scale, for example TULIPS1 (1995) contains short recordings of 12 speakers reading four first numerals in English (Movellan 1995). Bernstein Lipreading Corpus (1991) offers a more sizable language material (954 sentences, dictionary of 1000 words), however it contains recordings of only two speakers (Bernstein 1991).
The Multi Modal Verification for Teleservices and Security applications corpus (M2VTS) (Pigeon and Vandendorpe 1997), which was published in 1997, included additional recordings of head rotations in four directions - left to right, up and down (yaw, pitch), and an intentionally degraded recording material, but when compared to DAVID-BT, it is limited by small sample size and by the used language material, because it consists of recordings of 37 speakers uttering only numerals (from 0 to 9) recorded in five sessions.
CUAVE (Clemson University Audio Visual Experiments), database designed by Patterson et al. (2002) was focused on availability of the database (as it was the first corpus fitting on only one DVD disc) and realistic recording conditions. It was designed to enhance research in audio-visual speech recognition immune to speaker movement and capable of distinguishing multiple speakers simultaneously. The database consists of two sections, containing individual speakers and speaker pairs. The first part contains recordings of 36 speakers, uttering isolated or connected numeral sequences while remaining stationary or moving (side-to-side, back-and-forth, head tilting). The second part of the database included 20 pairs of speakers for testing multispeaker solutions. The two speakers are always visible in the shot. Scenarios include speakers uttering numeral sequences one after another, and then simultaneously. The recording environment was controlled, including uniform illumination and green background. The major setback of this database is its limited dictionary.
The corpus developed by Wong et al. (2011) UNMC-VIER (Wong et al. 2011), is described as a multi-purpose one, suitable for face or speech recognition. It attempts to address the shortcomings of preceding databases, and it introduces multiple simultaneous visual variations in video recordings. Those include: illumination, facial expression, head poses and image quality (an example combination: illumination + head pose, facial expression + low video quality). The audio part also has a changing component, namely the utterances are spoken in slow and in normal rate of speech to improve the learning of audio-visual recognition algorithms. Language material is based on the XM2VTS sentences (11 sentences used) and is accompanied by a sequence of numerals. The database includes recordings of 123 speakers in many configurations (two recording sessions per speaker - in controlled and uncontrolled environment, 11 repetitions of language material per speaker).
The MOBIO database, developed by Marcel et al. (2012), is a unique audio-visual corpus, as it was captured almost exclusively using mobile devices. It is composed of over 61 h of recordings of 150 speakers. The language material included a set of responses to short questions, also responses in free speech, and pre-defined text. The very first MOBIO recording session was recorded using a laptop computer, while all the other data were captured by a mobile phone. As the recording device was held by the user, the microphone and camera were used in an uncontrolled manner. This resulted in a high variability of pose and illumination of the speaker together with variations in the quality of speech and acoustic conditions. The MOBIO database delivers a set of realistic recordings, but it is mostly applicable to mobile-based systems. 2ff7e9595c
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