Visual speech recognition and utterance segmentation based on mouth movement

This paper presents a vision-based approach to recognize speech without evaluating the acoustic signals. The proposed technique combines motion features and support vector machines (SVMs) to classify utterances. Segmentation of utterances is important in a visual speech recognition system. This research proposes a video segmentation method to detect the start and end frames of isolated utterances from an image sequence. Frames that correspond to `speaking' and `silence' phases are identified based on mouth movement information. The experimental results demonstrate that the proposed visual speech recognition technique yields high accuracy in a phoneme classification task. Potential applications of such a system are, e.g., human computer interface (HCI) for mobility-impaired users, lip-reading mobile phones, in-vehicle systems, and improvement of speech-based computer control in noisy environments

CLASSIFICATION OF VISEMES USING VISUAL CUES

Studies have shown that visual features extracted from the lips of a speaker (visemes) can be used to automatically classify the visual representation of phonemes. Different visual features were extracted from the audio-visual recordings of a set of phonemes and used to define Linear Discriminant Analysis (LDA) functions to classify the phonemes. . Audio-visual recordings from 18 speakers of Native American English for 12 Vowel-Consonant-Vowel (VCV) sounds were obtained using the consonants /b,v,w,ð,d,z/ and the vowels /ɑ,i/. The visual features used in this study were related to the lip height, lip width, motion in upper lips and the rate at which lips move while producing the VCV sequences. Features extracted from half of the speakers were used to design the classifier and features extracted from the other half were used in testing the classifiers.When each VCV sound was treated as an independent class, resulting in 12 classes, the percentage of correct recognition was 55.3% in the training set and 43.1% in the testing set. This percentage increased as classes were merged based on the level of confusion appearing between them in the results. When the same consonants with different vowels were treated as one class, resulting in 6 classes, the percentage of correct classification was 65.2% in the training set and 61.6% in the testing set. This is consistent with psycho-visual experiments in which subjects were unable to distinguish between visemes associated with VCV words with the same consonant but different vowels. When the VCV sounds were grouped into 3 classes, the percentage of correct classification in the training set was 84.4% and 81.1% in the testing set.In the second part of the study, linear discriminant functions were developed for every speaker resulting in 18 different sets of LDA functions. For every speaker, five VCV utterances were used to design the LDA functions, and 3 different VCV utterances were used to test these functions. For the training data, the range of correct classification for the 18 speakers was 90-100% with an average of 96.2%. For the testing data, the range of correct classification was 50-86% with an average of 68%.A step-wise linear discriminant analysis evaluated the contribution of different features towards the dissemination problem. The analysis indicated that classifiers using only the top 7 features in the analysis had a performance drop of 2-5%. The top 7 features were related to the shape of the mouth and the rate of motion of lips when the consonant in the VCV sequence was being produced. Results of this work showed that visual features extracted from the lips can separate the visual representation of phonemes into different classes

Lip Motion Pattern Recognition for Indonesian Syllable Pronunciation Utilizing Hidden Markov Model Method

A speech therapeutic tool had been developed to help Indonesian deaf kids learn how to pronounce words correctly. The applied technique utilized lip movement frames captured by a camera and inputted them in to a pattern recognition module which can differentiate between different vowel phonemes pronunciation in Indonesian language. In this paper, we used one dimensional Hidden Markov Model (HMM) method for pattern recognition module. The feature used for the training and test data were composed of six key-points of 20 sequential frames representing certain phonemes. Seventeen Indonesian phonemes were chosen from the words usually used by deaf kid special school teachers for speech therapy. The results showed that the recognition rates varied on different phonemes articulation, ie. 78% for bilabial/palatal phonemes and 63% for palatal only phonemes. The condition of the lips also had effect on the result, where female with red lips has 0.77 correlation coefficient, compare to 0.68 for pale lips and 0.38 for male with mustaches

Changes in the McGurk Effect Across Phonetic Contexts

To investigate the process underlying audiovisual speech perception, the McGurk illusion was examined across a range of phonetic contexts. Two major changes were found. First, the frequency of illusory /g/ fusion percepts increased relative to the frequency of illusory /d/ fusion percepts as vowel context was shifted from /i/ to /a/ to /u/. This trend could not be explained by biases present in perception of the unimodal visual stimuli. However, the change found in the McGurk fusion effect across vowel environments did correspond systematically with changes in second format frequency patterns across contexts. Second, the order of consonants in illusory combination percepts was found to depend on syllable type. This may be due to differences occuring across syllable contexts in the timecourses of inputs from the two modalities as delaying the auditory track of a vowel-consonant stimulus resulted in a change in the order of consonants perceived. Taken together, these results suggest that the speech perception system either fuses audiovisual inputs into a visually compatible percept with a similar second formant pattern to that of the acoustic stimulus or interleaves the information from different modalities, at a phonemic or subphonemic level, based on their relative arrival times.National Institutes of Health (R01 DC02852

The Natural Statistics of Audiovisual Speech

Humans, like other animals, are exposed to a continuous stream of signals, which are dynamic, multimodal, extended, and time varying in nature. This complex input space must be transduced and sampled by our sensory systems and transmitted to the brain where it can guide the selection of appropriate actions. To simplify this process, it's been suggested that the brain exploits statistical regularities in the stimulus space. Tests of this idea have largely been confined to unimodal signals and natural scenes. One important class of multisensory signals for which a quantitative input space characterization is unavailable is human speech. We do not understand what signals our brain has to actively piece together from an audiovisual speech stream to arrive at a percept versus what is already embedded in the signal structure of the stream itself. In essence, we do not have a clear understanding of the natural statistics of audiovisual speech. In the present study, we identified the following major statistical features of audiovisual speech. First, we observed robust correlations and close temporal correspondence between the area of the mouth opening and the acoustic envelope. Second, we found the strongest correlation between the area of the mouth opening and vocal tract resonances. Third, we observed that both area of the mouth opening and the voice envelope are temporally modulated in the 2–7 Hz frequency range. Finally, we show that the timing of mouth movements relative to the onset of the voice is consistently between 100 and 300 ms. We interpret these data in the context of recent neural theories of speech which suggest that speech communication is a reciprocally coupled, multisensory event, whereby the outputs of the signaler are matched to the neural processes of the receiver

Artificial Vocal Learning guided by Phoneme Recognition and Visual Information

This paper introduces a paradigm shift regarding vocal learning simulations, in which the communicative function of speech acquisition determines the learning process and intelligibility is considered the primary measure of learning success. Thereby, a novel approach for artificial vocal learning is presented that utilizes deep neural network-based phoneme recognition in order to calculate the speech acquisition objective function. This function guides a learning framework that involves the state-of-the-art articulatory speech synthesizer VocalTractLab as the motor-to-acoustic forward model. In this way, an extensive set of German phonemes, including most of the consonants and all stressed vowels, was produced successfully. The synthetic phonemes were rated as highly intelligible by human listeners. Furthermore, it is shown that visual speech information, such as lip and jaw movements, can be extracted from video recordings and be incorporated into the learning framework as an additional loss component during the optimization process. It was observed that this visual loss did not increase the overall intelligibility of phonemes. Instead, the visual loss acted as a regularization mechanism that facilitated the finding of more biologically plausible solutions in the articulatory domain

Towards an Integrative Information Society: Studies on Individuality in Speech and Sign

The flow of information within modern information society has increased rapidly over the last decade. The major part of this information flow relies on the individual’s abilities to handle text or speech input. For the majority of us it presents no problems, but there are some individuals who would benefit from other means of conveying information, e.g. signed information flow. During the last decades the new results from various disciplines have all suggested towards the common background and processing for sign and speech and this was one of the key issues that I wanted to investigate further in this thesis. The basis of this thesis is firmly within speech research and that is why I wanted to design analogous test batteries for widely used speech perception tests for signers – to find out whether the results for signers would be the same as in speakers’ perception tests. One of the key findings within biology – and more precisely its effects on speech and communication research – is the mirror neuron system. That finding has enabled us to form new theories about evolution of communication, and it all seems to converge on the hypothesis that all communication has a common core within humans. In this thesis speech and sign are discussed as equal and analogical counterparts of communication and all research methods used in speech are modified for sign. Both speech and sign are thus investigated using similar test batteries. Furthermore, both production and perception of speech and sign are studied separately. An additional framework for studying production is given by gesture research using cry sounds. Results of cry sound research are then compared to results from children acquiring sign language. These results show that individuality manifests itself from very early on in human development. Articulation in adults, both in speech and sign, is studied from two perspectives: normal production and re-learning production when the apparatus has been changed. Normal production is studied both in speech and sign and the effects of changed articulation are studied with regards to speech. Both these studies are done by using carrier sentences. Furthermore, sign production is studied giving the informants possibility for spontaneous speech. The production data from the signing informants is also used as the basis for input in the sign synthesis stimuli used in sign perception test battery. Speech and sign perception were studied using the informants’ answers to questions using forced choice in identification and discrimination tasks. These answers were then compared across language modalities. Three different informant groups participated in the sign perception tests: native signers, sign language interpreters and Finnish adults with no knowledge of any signed language. This gave a chance to investigate which of the characteristics found in the results were due to the language per se and which were due to the changes in modality itself. As the analogous test batteries yielded similar results over different informant groups, some common threads of results could be observed. Starting from very early on in acquiring speech and sign the results were highly individual. However, the results were the same within one individual when the same test was repeated. This individuality of results represented along same patterns across different language modalities and - in some occasions - across language groups. As both modalities yield similar answers to analogous study questions, this has lead us to providing methods for basic input for sign language applications, i.e. signing avatars. This has also given us answers to questions on precision of the animation and intelligibility for the users – what are the parameters that govern intelligibility of synthesised speech or sign and how precise must the animation or synthetic speech be in order for it to be intelligible. The results also give additional support to the well-known fact that intelligibility in fact is not the same as naturalness. In some cases, as shown within the sign perception test battery design, naturalness decreases intelligibility. This also has to be taken into consideration when designing applications. All in all, results from each of the test batteries, be they for signers or speakers, yield strikingly similar patterns, which would indicate yet further support for the common core for all human communication. Thus, we can modify and deepen the phonetic framework models for human communication based on the knowledge obtained from the results of the test batteries within this thesis.Siirretty Doriast