Dealing with Phrase Level Co-Articulation (PLC) in speech recognition: A first approach

Whereas nowadays within-word co-articulation effects are usually sufficiently dealt with in automatic speech recognition, this is not always the case with phrase level co-articulation effects (PLC). This paper describes a first approach in dealing with phrase level co-articulation by applying these rules on the reference transcripts used for training our recogniser and by adding a set of temporary PLC phones that later on will be mapped on the original phones. In fact we temporarily break down acoustic context into a general and a PLC context. With this method, more robust models could be trained because phones that are confused due to PLC effects like for example /v/-/f/ and /z/-/s/, receive their own models. A first attempt to apply this method is described

DEALING WITH PHRASE LEVEL CO-ARTICULATION (PLC) IN SPEECH RECOGNITION: A FIRST APPROACH

Whereas nowadays within-word co-articulation effects are usually sufficiently dealt with in automatic speech recognition, this is not always the case with phrase level co-articulation effects (PLC). This paper describes a first approach in dealing with phrase level co-articulation by applying these rules on the reference transcripts used for training our recogniser and by adding a set of temporary PLC phones that later on will be mapped on the original phones. In fact we temporarily break down acoustic context into a general and a PLC context. With this method, more robust models could be trained because phones that are confused due to PLC effects like for example /v/-/f / and /z/-/s/, receive their own models. A first attempt to apply this method is described

Cationic ansa-(η5-Cyclopentadienyl)(η6-arene) Complexes of Titanium

The half-sandwich titanium trimethyl complex (η5-C5H4CMe2Ar)TiMe3 (Ar = 3,5-Me2C6H3) reacts with the Lewis acid B(C6F5)3 to give the ionic TiIV ansa-cyclopentadienyl-arene complex [(η5,η6-C5H4CMe2Ar)TiMe2][MeB(C6F5)3]. In bromobenzene solvent, addition of more B(C6F5)3 leads to C6F5/Me exchange and, subsequently, to formation of an unusual dimeric TiIII dicationic species, {[(η5,η6-C5H4CMe2Ar)Ti(μ-Br)]2}[B(C6F5)4]2, which was structurally characterized. Its formation involves reduction of the transition-metal center, solvent C–Br cleavage and perfluoroaryl-group scrambling.

Dealing With Phrase Level Co-Articulation (PLC) In Speech Recognition: A First Approach

Whereas nowadays within-word co-articulation effects are usually sufficiently dealt with in automatic speech recognition, this is not always the case with phrase level co-articulation effects (PLC). This paper describes a first approach in dealing with phrase level co-articulation by applying these rules on the reference transcripts used for training our recogniser and by adding a set of temporary PLC phones that later on will be mapped on the original phones. In fact we temporarily break down acoustic context into a general and a PLC context. With this method, more robust models could be trained because phones that are confused due to PLC effects like for example /v/-/f/ and /z/-/s/, receive their own models. A first attempt to apply this method is described. 1. INTRODUCTION The DRUID 1 project (Document Retrieval Using Intelligent Disclosure), a collaboration of CTIT 2 /University of Twente, TNO 3 and CWI 4 , aims at the development of tools for the indexing of multimedia c..

Dealing With Phrase Level Co-Articulation (plc)

Whereas nowadays within-word co-articulation effects are usually sufficiently dealt with in automatic speech recognition, this is not always the case with phrase level co-articulation effects (PLC). This paper describes a first approach in dealing with phrase level co-articulation by applying these rules on the reference transcripts used for training our recogniser and by adding a set of temporary PLC phones that later on will be mapped on the original phones. In fact we temporarily break down acoustic context into a general and a PLC context. With this method, more robust models could be trained because phones that are confused due to PLC effects like for example /v/-/f/ and /z/-/s/, receive their own models. A first attempt to apply this method is described

Recognizing hotspots in Brief Eclectic Psychotherapy for PTSD by text and audio mining

Background: Identifying and addressing hotspots is a key element of imaginal exposure in Brief Eclectic Psychotherapy for PTSD (BEPP). Research shows that treatment effectiveness is associated with focusing on these hotspots and that hotspot frequency and characteristics may serve as indicators for treatment success. Objective: This study aims to develop a model to automatically recognize hotspots based on text and speech features, which might be an efficient way to track patient progress and predict treatment efficacy. Method: A multimodal supervised classification model was developed based on analog tape recordings and transcripts of imaginal exposure sessions of 10 successful and 10 non-successful treatment completers. Data mining and machine learning techniques were used to extract and select text (e.g. words and word combinations) and speech (e.g. speech rate, pauses between words) features that distinguish between ‘hotspot’ (N = 37) and ‘non-hotspot’ (N = 45) phases during exposure sessions. Results: The developed model resulted in a high training performance (mean F1-score of 0.76) but a low testing performance (mean F1-score = 0.52). This shows that the selected text and speech features could clearly distinguish between hotspots and non-hotspots in the current data set, but will probably not recognize hotspots from new input data very well. Conclusions: In order to improve the recognition of new hotspots, the described methodology should be applied to a larger, higher quality (digitally recorded) data set. As such this study should be seen mainly as a proof of concept, demonstrating the possible application and contribution of automatic text and audio analysis to therapy process research in PTSD and mental health research in general