Phoneme and sentence-level ensembles for speech recognition

We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the phoneme level and one at the utterance level, with a phoneme-level bagging scheme. We control for many parameters and other choices, such as the state inference scheme used. In an unbiased experiment, we clearly show that the gain of boosting methods compared to a single hidden Markov model is in all cases only marginal, while bagging significantly outperforms all other methods. We thus conclude that bagging methods, which have so far been overlooked in favour of boosting, should be examined more closely as a potentially useful ensemble learning technique for speech recognition

A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits

Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children. Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually acquired while operating rather than being abundant and fully available as the classical machine learning scenarios. The experimental analyses show that the proprioceptive information from the robot fingers can improve network accuracy in the recognition of handwritten Arabic digits when training examples and epochs are few. This result is comparable to brain imaging and longitudinal studies with young children. In conclusion, these findings also support the relevance of the embodiment in the case of artificial agents’ training and show a possible way for the humanization of the learning process, where the robotic body can express the internal processes of artificial intelligence making it more understandable for humans

Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usability and perceived quality. Most NLG systems in common use employ rules and heuristics and tend to generate rigid and stylised responses without the natural variation of human language. They are also not easily scaled to systems covering multiple domains and languages. This paper presents a statistical language generator based on a semantically controlled Long Short-term Memory (LSTM) structure. The LSTM generator can learn from unaligned data by jointly optimising sentence planning and surface realisation using a simple cross entropy training criterion, and language variation can be easily achieved by sampling from output candidates. With fewer heuristics, an objective evaluation in two differing test domains showed the proposed method improved performance compared to previous methods. Human judges scored the LSTM system higher on informativeness and naturalness and overall preferred it to the other systems.Comment: To be appear in EMNLP 201

Enhanced amplitude modulations contribute to the Lombard intelligibility benefit: Evidence from the Nijmegen Corpus of Lombard Speech

Speakers adjust their voice when talking in noise, which is known as Lombard speech. These acoustic adjustments facilitate speech comprehension in noise relative to plain speech (i.e., speech produced in quiet). However, exactly which characteristics of Lombard speech drive this intelligibility benefit in noise remains unclear. This study assessed the contribution of enhanced amplitude modulations to the Lombard speech intelligibility benefit by demonstrating that (1) native speakers of Dutch in the Nijmegen Corpus of Lombard Speech (NiCLS) produce more pronounced amplitude modulations in noise vs. in quiet; (2) more enhanced amplitude modulations correlate positively with intelligibility in a speech-in-noise perception experiment; (3) transplanting the amplitude modulations from Lombard speech onto plain speech leads to an intelligibility improvement, suggesting that enhanced amplitude modulations in Lombard speech contribute towards intelligibility in noise. Results are discussed in light of recent neurobiological models of speech perception with reference to neural oscillators phase-locking to the amplitude modulations in speech, guiding the processing of speech

Using Semantic Ambiguity Instruction to Improve Third Graders\u27 Metalinguistic Awareness and Reading Comprehension: An Experimental Study

An experiment examined whether metalinguistic awareness involving the detection of semantic ambiguity can be taught and whether this instruction improves students\u27 reading comprehension. Lower socioeconomic status third graders (M age = 8 years, 7 months) from a variety of cultural backgrounds (N = 46) were randomly assigned to treatment and control groups. Those receiving metalinguistic ambiguity instruction learned to analyze multiple meanings of words and sentences in isolation, in riddles, and in text taken from the Amelia Bedelia series (Parish, 1979, 988). The control group received a book-reading and discussion treatment to provide special attention and to rule out Hawthorne effects. Results showed that metalinguistic ambiguity instruction was effective in teaching students to identify multiple meanings of homonyms and ambiguous sentences and to detect inconsistencies in text. Moreover, this training enhanced students\u27 reading com prehension on a paragraph-completion task but not on a multiple-choice passage-recall task, possibly because the two tests differ in the array of linguistic or cognitive correlates influencing performance. Comprehension monitoring was not found to mediate the relationship between ambiguity instruction and reading comprehension. Results carry implications for the use of language-based methods to improve reading comprehension in the classroom

Autism, the Integrations of 'Difference' and the Origins of Modern Human Behaviour

It is proposed here that the archaeological evidence for the emergence of 'modern behaviour' (160,000-40,000 bp) can best be explained as the rise of cognitive variation within populations through social mechanisms for integrating 'different minds', rather than by the development of a single 'modern human mind'. Autism and the autistic spectrum within human populations are used as an example of 'different minds' which when integrated within society can confer various selective benefits. It is proposed that social mechanisms for incorporating autistic difference are visible in the archaeological record and that these develop sporadically from 160,000 years bp in association with evidence for their consequences in terms of technological innovations, improved efficiency in technological and natural spheres and innovative thinking. Whilst other explanations for the emergence Of modern human behaviour may also contribute to observed changes, it is argued that the incorporation of cognitive differences played a significant role in the technological, social and symbolic expression of 'modern' behaviour