Speech-in-noise perception is linked to rhythm production skills in adult percussionists and non-musicians

Speech rhythms guide perception, especially in noise. We recently revealed that percussionists outperform non-musicians in speech-in-noise perception, with better speech-in-noise perception associated with better rhythm discrimination across a range of rhythmic expertise. Here, we consider rhythm production skills, specifically drumming to a beat (metronome or music) and to sequences (metrical or jittered patterns), as well as speech-in-noise perception in adult percussionists and non-musicians. Given the absence of a regular beat in speech, we hypothesise that processing of sequences is more important for speech-in-noise perception than the ability to entrain to a regular beat. Consistent with our hypothesis, we find that the sequence-based drumming measures predict speech-in-noise perception, above and beyond hearing thresholds and IQ, whereas the beat-based measures do not. Outcomes suggest temporal patterns may help disambiguate speech under degraded listening conditions, extending theoretical considerations about speech rhythm to the everyday challenge of listening in noise

Speech rhythm: a metaphor?

Is speech rhythmic? In the absence of evidence for a traditional view that languages strive to coordinate either syllables or stress-feet with regular time intervals, we consider the alternative that languages exhibit contrastive rhythm subsisting merely in the alternation of stronger and weaker elements. This is initially plausible, particularly for languages with a steep ‘prominence gradient’, i.e. a large disparity between stronger and weaker elements; but we point out that alternation is poorly achieved even by a ‘stress-timed’ language such as English, and, historically, languages have conspicuously failed to adopt simple phonological remedies that would ensure alternation. Languages seem more concerned to allow ‘syntagmatic contrast’ between successive units and to use durational effects to support linguistic functions than to facilitate rhythm. Furthermore, some languages (e.g. Tamil, Korean) lack the lexical prominence which would most straightforwardly underpin prominence alternation. We conclude that speech is not incontestibly rhythmic, and may even be antirhythmic. However, its linguistic structure and patterning allow the metaphorical extension of rhythm in varying degrees and in different ways depending on the language, and that it is this analogical process which allows speech to be matched to external rhythms

Perceptual learning of time-compressed and natural fast speech

Speakers vary their speech rate considerably during a conversation, and listeners are able to quickly adapt to these variations in speech rate. Adaptation to fast speech rates is usually measured using artificially time-compressed speech. This study examined adaptation to two types of fast speech: artificially time-compressed speech and natural fast speech. Listeners performed a speeded sentence verification task on three series of sentences: normal-speed sentences, time-compressed sentences, and natural fast sentences. Listeners were divided into two groups to evaluate the possibility of transfer of learning between the time-compressed and natural fast conditions. The first group verified the natural fast before the time-compressed sentences, while the second verified the time-compressed before the natural fast sentences. The results showed transfer of learning when the time-compressed sentences preceded the natural fast sentences, but not when natural fast sentences preceded the time-compressed sentences. The results are discussed in the framework of theories on perceptual learning. Second, listeners show adaptation to the natural fast sentences, but performance for this type of fast speech does not improve to the level of time-compressed sentences

On the Tail of the Scottish Vowel Length Rule in Glasgow

One of the most famous sound features of Scottish English is the short/long timing alternation of /i u ai/vowels, which depends on the morpho-phonemic environment, and is known of as the Scottish Vowel Length Rule (SVLR). These alternations make the status of vowel quantity in Scottish English (quasi-)phonemic but are also susceptible to change, particularly in situations of intense sustained dialect contact with Anglo-English. Does the SVLR change in Glasgow where dialect contact at the community level is comparably low? The present study sets out to tackle this question, and tests two hypotheses involving (1) external influences due to dialect-contact and (2) internal, prosodically-induced factors of sound change. Durational analyses of /i u a/ were conducted on a corpus of spontaneous Glaswegian speech from the 1970s and 2000s, and four speaker groups were compared, two of middle-aged men, and two of adolescent boys. Our hypothesis that the development of the SVLR over time may be internally constrained and interact with prosody was largely confirmed. We observed weakening effects in its implementation which were localised in phrase-medial unaccented positions in all speaker groups, and in phrase-final positions in the speakers born after the Second World War. But unlike some other varieties of Scottish or Northern English which show weakening of the Rule under a prolonged contact with Anglo-English, dialect contact seems to be having less impact on the durational patterns in Glaswegian vernacular, probably because of the overall reduced potential for a regular, everyday contact in the West given the different demographies

Prosodic tools for language learning

In this paper we will be concerned with the role played by prosody in language learning and by the speech technology already available as commercial product or as prototype, capable to cope with the task of helping language learner in improving their knowledge of a second language from the prosodic point of view. The paper has been divided into two separate sections: Section One, dealing with Rhythm and all related topics; Section Two dealing with Intonation. In the Introduction we will argue that the use of ASR (Automatic Speech Recognition) as Teaching Aid should be under-utilized and should be targeted to narrowly focussed spoken exercises, disallowing open-ended dialogues, in order to ensure consistency of evaluation. Eventually, we will support the conjoined use of ASR technology and prosodic tools to produce GOP useable for linguistically consistent and adequate feedback to the student. This will be illustrated by presenting State of the Art for both sections, with systems well documented in the scientific literature of the respective field. In order to discuss the scientific foundations of prosodic analysis we will present data related to English and Italian and make comparisons to clarify the issues at hand. In this context, we will also present the Prosodic Module of a courseware for computer-assisted foreign language learning called SLIM—an acronym for Multimedia Interactive Linguistic Software, developed at the University of Venice (Delmonte et al. in Convegno GFS-AIA, pp. 47–58, 1996a; Ed-Media 96, AACE, pp. 326–333, 1996b). The Prosodic Module has been created in order to deal with the problem of improving a student’s performance both in the perception and production of prosodic aspects of spoken language activities. It is composed of two different sets of Learning Activities, the first one dealing with phonetic and prosodic problems at word level and at syllable level; the second one dealing with prosodic aspects at phonological phrase and utterance suprasegmental level. The main goal of Prosodic Activities is to ensure consistent and pedagogically sound feedback to the student intending to improve his/her pronunciation in a foreign language

It's not what you say but the way that you say it: an fMRI study of differential lexical and non-lexical prosodic pitch processing

<p>Abstract</p> <p>Background</p> <p>This study aims to identify the neural substrate involved in prosodic pitch processing. Functional magnetic resonance imaging was used to test the premise that prosody pitch processing is primarily subserved by the right cortical hemisphere.</p> <p>Two experimental paradigms were used, firstly pairs of spoken sentences, where the only variation was a single internal phrase pitch change, and secondly, a matched condition utilizing pitch changes within analogous tone-sequence phrases. This removed the potential confounder of lexical evaluation. fMRI images were obtained using these paradigms.</p> <p>Results</p> <p>Activation was significantly greater within the right frontal and temporal cortices during the tone-sequence stimuli relative to the sentence stimuli.</p> <p>Conclusion</p> <p>This study showed that pitch changes, stripped of lexical information, are mainly processed by the right cerebral hemisphere, whilst the processing of analogous, matched, lexical pitch change is preferentially left sided. These findings, showing hemispherical differentiation of processing based on stimulus complexity, are in accord with a 'task dependent' hypothesis of pitch processing.</p

Timing in talking: What is it used for, and how is it controlled?

In the first part of the paper, we summarize the linguistic factors that shape speech timing patterns, including the prosodic structures which govern them, and suggest that speech timing patterns are used to aid utterance recognition. In the spirit of optimal control theory, we propose that recognition requirements are balanced against requirements such as rate of speech and style, as well as movement costs, to yield (near-)optimal planned surface timing patterns; additional factors may influence the implementation of that plan. In the second part of the paper, we discuss theories of timing control in models of speech production and motor control. We present three types of evidence that support models of speech production that involve extrinsic timing. These include (i) increasing variability with increases in interval duration, (ii) evidence that speakers refer to and plan surface durations, and (iii) independent timing of movement onsets and offsets