| 1. |
- Altosaar, Toomas, et al.
(författare)
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A Speech Corpus for Modeling Language Acquisition : CAREGIVER
- 2010
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Ingår i: 7th International Conference on Language Resources and Evaluation (LREC) 2010, Valletta, Malta. - : European Language Resources Association (ELRA). - 9782951740860 ; , s. 1062-1068
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Konferensbidrag (refereegranskat)abstract
- A multi-lingual speech corpus used for modeling language acquisition called CAREGIVER has been designed and recorded within the framework of the EU funded Acquisition of Communication and Recognition Skills (ACORNS) project. The paper describes the motivation behind the corpus and its design by relying on current knowledge regarding infant language acquisition. Instead of recording infants and children, the voices of their primary and secondary caregivers were captured in both infant-directed and adult-directed speech modes over four languages in a read speech manner. The challenges and methods applied to obtain similar prompts in terms of complexity and semantics across different languages, as well as the normalized recording procedures employed at different locations, is covered. The corpus contains nearly 66000 utterance based audio files spoken over a two-year period by 17 male and 17 female native speakers of Dutch, English, Finnish, and Swedish. An orthographical transcription is available for every utterance. Also, time-aligned word and phone annotations for many of the sub-corpora also exist. The CAREGIVER corpus will be published via ELRA.
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| 2. |
- Chatterjee, Saikat, et al.
(författare)
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Auditory model based optimization of MFCCs improves automatic speech recognition performance
- 2009
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Ingår i: INTERSPEECH 2009. - 9781615676927 ; , s. 2943-2946
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Konferensbidrag (refereegranskat)abstract
- Using a spectral auditory model along with perturbation based analysis, we develop a new framework to optimize a set of features such that it emulates the behavior of the human auditory system. The optimization is carried out in an off-line manner based on the conjecture that the local geometries of the feature domain and the perceptual auditory domain should be similar. Using this principle, we modify and optimize the static mel frequency cepstral coefficients (MFCCs) without considering any feedback from the speech recognition system. We show that improved recognition performance is obtained for any environmental condition, clean as well as noisy.
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| 3. |
- Dobnik, Simon, 1977, et al.
(författare)
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Investigating the role of priming and alignment of perspective in dialogue
- 2013
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Ingår i: Proceedings of the 17th Workshop on the Semantics and Pragmatics of Dialogue, Amsterdam, 16–18 December 2013, Amsterdam. - 2308-2275. ; 17, s. 182-184
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We examine the alignment of the primed frame of reference (FoR) for spatial descriptions over several utterances of a situated dialogue. We confirm the tendency of FoR alignment and that the intrinsic FoR is the most popular one independent of the priming.
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| 4. |
- Dobnik, Simon, 1977, et al.
(författare)
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Priming and Alignment of Frame of Reference in Situated Conversation
- 2014
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Ingår i: Proceedings of SemDial 2014 (DialWatt): The 18th Workshop on the Semantics and Pragmatics of Dialogue, Edinburgh, September 1-3, 2014. - 2308-2275. ; , s. 43-52
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Konferensbidrag (refereegranskat)abstract
- In this paper, we study how the frame of reference (FoR) or perspective is communicated in dialogue through mechanisms such as linguistic priming and alignment (Pickering and Garrod, 2004). In order to isolate the contribution of these mechanisms we deliberately work with a constrained artificial dialogue scenario. First we collect data that deal with human behaviour in interpreting descriptions that are ambiguous in terms of the FoR. From these interpretations we extract and identify strategies for FoR assignment in conversations which we then apply to generate descriptions and measure human agreement with the system. Our findings confirm that both speakers and hearers rely on such mechanisms in conversation.
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| 5. |
- Gugliermo, Simona, 1995-, et al.
(författare)
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Extracting Planning Domains from Execution Traces : a Progress Report
- 2023
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Konferensbidrag (refereegranskat)abstract
- One of the difficulties of using AI planners in industrial applications pertains to the complexity of writing planning domain models. These models are typically constructed by domain planning experts and can become increasingly difficult to codify for large applications. In this paper, we describe our ongoing research on a novel approach to automatically learn planning domains from previously executed traces using Behavior Trees as an intermediate human-readable structure. By involving human planning experts in the learning phase, our approach can benefit from their validation. This paper outlines the initial steps we have taken in this research, and presents the challenges we face in the future.
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| 6. |
- Gugliermo, Simona, 1995-, et al.
(författare)
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Learning Behavior Trees From Planning Experts Using Decision Tree and Logic Factorization
- 2023
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 8:6, s. 3534-3541
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Tidskriftsartikel (refereegranskat)abstract
- The increased popularity of Behavior Trees (BTs) in different fields of robotics requires efficient methods for learning BTs from data instead of tediously handcrafting them. Recent research in learning from demonstration reported encouraging results that this letter extends, improves and generalizes to arbitrary planning domains. We propose BT-Factor as a new method for learning expert knowledge by representing it in a BT. Execution traces of previously manually designed plans are used to generate a BT employing a combination of decision tree learning and logic factorization techniques originating from circuit design. We test BT-Factor in an industrially-relevant simulation environment from a mining scenario and compare it against a state-of-the-art BT learning method. The results show that our method generates compact BTs easy to interpret, and capable to capture accurately the relations that are implicit in the training data.
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| 7. |
- Koniaris, Christos
(författare)
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A study on selecting and optimizing perceptually relevant features for automatic speech recognition
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The performance of an automatic speech recognition (ASR) system strongly depends on the representation used for the front-end. If the extracted features do not include all relevant information, the performance of the classification stage is inherently suboptimal. This work is motivated by the fact that humans perform better at speech recognition than machines, particularly for noisy environments. The goal of this thesis is to make use of knowledge of human perception in the selection and optimization of speech features for speech recognition. Papers A and C show that robust feature selection for speech recognition can be based on models of the human auditory system. These papers show that maximizing the similarity of the Euclidian geometry of the features to the geometry of the perceptual domain is a powerful tool to select features. Whereas conventional methods optimize classification performance, the new feature selection method exploits knowledge implicit in the human auditory system, inheriting its robustness to varying environmental conditions. The proposed algorithm show how the feature set can be learned from perception only by establishing a measure of goodness for a given feature based on a perturbation analysis and distortion criteria derived from psycho-acoustic models. Experiments with a practical speech recognizer confirm the validity of the principle. In Paper B the perceptually relevant objective criterion is used to define new features. Again the motivation has its origin at the human peripheral auditory system which plays a major role to the input speech signal until it reaches the central auditory system of the brain where the recognition occurs. While many feature extraction techniques incorporate knowledge of the auditory system, the procedures are usually designed for a specific task, and they lack of the most recently gained knowledge on human hearing. Paper B shows an approach to improve mel frequency cepstrum coefficients (MFCCs) through off-line optimization. The method has three advantages: i) it is computational inexpensive, ii) it does not use the auditory model directly, thus avoiding its computational cost, and iii) importantly, it provides better recognition performance than traditional MFCCs for both clean and noisy conditions
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| 8. |
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| 9. |
- Koniaris, Christos, 1979-, et al.
(författare)
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Auditory and Dynamic Modeling Paradigms to Detect L2 Mispronunciations
- 2012
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Ingår i: 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012, Vol 1. - 9781622767595 ; , s. 898-901
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Konferensbidrag (refereegranskat)abstract
- This paper expands our previous work on automatic pronunciation error detection that exploits knowledge from psychoacoustic auditory models. The new system has two additional important features, i.e., auditory and acoustic processing of the temporal cues of the speech signal, and classification feedback from a trained linear dynamic model. We also perform a pronunciation analysis by considering the task as a classification problem. Finally, we evaluate the proposed methods conducting a listening test on the same speech material and compare the judgment of the listeners and the methods. The automatic analysis based on spectro-temporal cues is shown to have the best agreement with the human evaluation, particularly with that of language teachers, and with previous plenary linguistic studies.
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| 10. |
- Koniaris, Christos, 1979-, et al.
(författare)
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Auditory-model based robust feature selection for speech recognition
- 2010
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Ingår i: Journal of the Acoustical Society of America. - : Acoustical Society of America (ASA). - 0001-4966 .- 1520-8524. ; 127:2, s. EL73-EL79
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Tidskriftsartikel (refereegranskat)abstract
- It is shown that robust dimension-reduction of a feature set for speech recognition can be based on a model of the human auditory system. Whereas conventional methods optimize classification performance, the proposed method exploits knowledge implicit in the auditory periphery, inheriting its robustness. Features are selected to maximize the similarity of the Euclidean geometry of the feature domain and the perceptual domain. Recognition experiments using mel-frequency cepstral coefficients (MFCCs) confirm the effectiveness of the approach, which does not require labeled training data. For noisy data the method outperforms commonly used discriminant-analysis based dimension-reduction methods that rely on labeling. The results indicate that selecting MFCCs in their natural order results in subsets with good performance.
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