2 research outputs found
ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π»Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΠ½Π°ΡΠΈΠΎΠ½Π½ΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ ΡΠ»ΠΎΠ² Π² ΡΡΡΡΠΊΠΎΡΠ·ΡΡΠ½ΠΎΠΌ ΡΠ΅ΠΊΡΡΠ΅
The article presents a method of detecting prosodically prominent words, i.e. words that carry most of the information in the utterance. The method relies on lexical, grammatical and syntactic markers of prominence, and can be used in Text-to-Speech synthesis systems to make synthesized speech sound more natural.
Three different classification methods were used: Naive Bayes, Maximum Entropy and Conditional Random Fields models. The results of the experiments show that discriminative models provide more balanced values of the performance metrics, while the generative model is potentially more useful for detecting prominent words in speech signal.
The results of the study are comparable with the performances of similar systems developed for other languages, and in some cases surpass them.Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ ΠΈΠ½ΡΠΎΠ½Π°ΡΠΈΠΎΠ½Π½ΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΡΠ»ΠΎΠ², ΡΠΎ Π΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²Π°ΠΆΠ½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π² Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΠΈ. ΠΠ΅ΡΠΎΠ΄ ΠΎΠΏΠΈΡΠ°Π΅ΡΡΡ Π½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π»Π΅ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
, Π³ΡΠ°ΠΌΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΈΠ½ΡΠΎΠ½Π°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ, ΡΡΠΎ Π΄Π΅Π»Π°Π΅Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠΌ Π΅Π³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΡΠΈΠ½ΡΠ΅Π·Π° ΡΠ΅ΡΠΈ ΠΏΠΎ ΡΠ΅ΠΊΡΡΡ, Π³Π΄Π΅ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΈΠ½ΡΠΎΠ½Π°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΌΠΎΠΆΠ΅Ρ ΠΏΠΎΠ²ΡΡΠΈΡΡ Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΡΡΡ Π·Π²ΡΡΠ°Π½ΠΈΡ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠ΅ΡΠΈ.
Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π½Π΅Π·Π°Π²ΠΈΡΠΈΠΌΠΎ Π΄ΡΡΠ³ ΠΎΡ Π΄ΡΡΠ³Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΎΡΡ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ: Π½Π°ΠΈΠ²Π½Π°Ρ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ, ΠΌΠΎΠ΄Π΅Π»Ρ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΡΠ½ΡΡΠΎΠΏΠΈΠΈ ΠΈ ΡΡΠ»ΠΎΠ²Π½ΡΠ΅ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΠ΅ ΠΏΠΎΠ»Ρ. Π‘ΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ², ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π² Ρ
ΠΎΠ΄Π΅ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠΎΠ², ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΎ, ΡΡΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π²ΡΠΈΠ΅ΡΡ Π΄ΠΈΡΠΊΡΠΈΠΌΠΈΠ½Π°ΡΠΈΠ²Π½ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΡΡ ΡΠ±Π°Π»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΠΈ ΠΏΡΠΈΠΌΠ΅ΡΠ½ΠΎ ΡΠ°Π²Π½ΡΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΌΠ΅ΡΡΠΈΠΊ ΠΊΠ°ΡΠ΅ΡΡΠ²Π°, Π² ΡΠΎ Π²ΡΠ΅ΠΌΡ ΠΊΠ°ΠΊ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ²Π½Π°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎ Π±ΠΎΠ»Π΅Π΅ ΠΏΡΠΈΠ³ΠΎΠ΄Π½Π° Π΄Π»Ρ ΠΏΠΎΠΈΡΠΊΠ° ΠΈΠ½ΡΠΎΠ½Π°ΡΠΈΠΎΠ½Π½ΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΡΠ»ΠΎΠ² Π² ΡΠ΅ΡΠ΅Π²ΠΎΠΌ ΡΠΈΠ³Π½Π°Π»Π΅.
Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΠ΅ Π² ΡΡΠ°ΡΡΠ΅, ΡΡΠ°Π²Π½ΠΈΠΌΡ ΠΈ Π² Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
ΡΠ»ΡΡΠ°ΡΡ
ΠΏΡΠ΅Π²ΠΎΡΡ
ΠΎΠ΄ΡΡ Π°Π½Π°Π»ΠΎΠ³ΠΈΡΠ½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠ΅ Π΄Π»Ρ Π΄ΡΡΠ³ΠΈΡ
ΡΠ·ΡΠΊΠΎΠ²
Acoustic identification of sentence accent in speakers with dysarthria : cross-population validation and severity related patterns
Dysprosody is a hallmark of dysarthria, which can affect the intelligibility and naturalness of speech. This includes sentence accent, which helps to draw listenersβ attention to important information in the message. Although some studies have investigated this feature, we currently lack properly validated automated procedures that can distinguish between subtle performance differences observed across speakers with dysarthria. This study aims for cross-population validation of a set of acoustic features that have previously been shown to correlate with sentence accent. In addition, the impact of dysarthria severity levels on sentence accent production is investigated. Two groups of adults were analysed (Dutch and English speakers). Fifty-eight participants with dysarthria and 30 healthy control participants (HCP) produced sentences with varying accent positions. All speech samples were evaluated perceptually and analysed acoustically with an algorithm that extracts ten meaningful prosodic features and allows a classification between accented and unaccented syllables based on a linear combination of these parameters. The data were statistically analysed using discriminant analysis. Within the Dutch and English dysarthric population, the algorithm correctly identified 82.8 and 91.9% of the accented target syllables, respectively, indicating that the capacity to discriminate between accented and unaccented syllables in a sentence is consistent with perceptual impressions. Moreover, different strategies for accent production across dysarthria severity levels could be demonstrated, which is an important step toward a better understanding of the nature of the deficit and the automatic classification of dysarthria severity using prosodic features