These vocal patterns are perceived and understood by listeners during conversation. Humans convey emotional information both intentionally and unintentionally via speech patterns. In both cases, it is worth stressing that only acoustic parameters derived directly from speech waveforms are exploited.Ībstract Machine-based emotional intelligence is a requirement for more natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Two different prominence detectors were studied and developed: the first uses a training corpus to set up thresholds properly, while the second uses a pure unsupervised method. This paper shows that a careful measurement of these acoustic parameters, as well as the identification of their connection to prosodic parameters, makes it possible to build an automatic system capable of identifying prominent syllables in utterances with performance comparable with the inter-human agreement reported in the literature. Pitch accent is acoustically connected with fundamental frequency (F0) movements and overall syllable energy, whereas stress exhibits a strong correlation with syllable nuclei duration and mid-to-high-frequency emphasis. Prosodic prominence involves two different prosodic features: pitch accent and stress accent. This paper presents a study on the automatic detection of prosodic prominence in continuous speech, with particular reference to American English, but with good prospects of application to other languages. In particular they are useful for a wide variety of tasks: automatic recognition of spontaneous speech, automatic enhancement of speech-generation systems, solving ambiguities in natural language interpretation, the construction of large annotated language resources, such as prosodically tagged speech corpora, and teaching languages to foreign students using Computer Aided Language Learning (CALL) systems. A precise identification of prosodic phenomena and the construction of tools able to properly manage such phenomena are essential steps to disambiguate the meaning of certain utterances. Index Terms-Audio, evaluation, melody transcription, music. Melodies transcribed at this level are readily recognizable, and show promise for practical applications. For our definition of melody, current systems can achieve around 70 % correct transcription at the frame level, including distinguishing between the presence or absence of the melody. We go on to describe the results of full-scale evaluations of melody transcription systems conducted in 20, including an overview of the systems submitted, details of how the evaluations were conducted, and a discussion of the results.
#Rapt define how to
We discuss melody-roughly, the part a listener might whistle or hum-as one such reduced descriptor of music audio, and consider how to define it, and what use it might be. For example, pause and pitch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues dominate for natural conversation.Ībstract-Although the process of analyzing an audio recording of a music performance is complex and difficult even for a human listener, there are limited forms of information that may be tractably extracted and yet still enable interesting applications. Finally, cue usage is task and corpus dependent. Inspection reveals that the prosodic models capture language-independent boundary indicators described in the literature.
Across tasks and corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. The prosodic model achieves comparable performance with significantly less training data, and requires no hand-labeling of prosodic events. Results show that the prosodic model alone performs on par with, or better than, word-based statistical language models-for both true and automatically recognized words in news speech.
Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with word-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. We investigate the use of prosody (informationgleaned from the timing and melody of speech) for these tasks. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken language. A crucial step in processing speech audio data for information-extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units.
0 kommentar(er)
