Consistency in transcription and labelling of German intonation with GToBI

A diverse set of speech data was labelled in three sites by 13 transcribers with differing levels of expertise, using GToBI, a consensus transcription system for German intonation. Overall inter-transcriber -consistency suggests that, with training, labellers can acquire sufficient skill with GToBI for large-scale database labelling. 1

Comparing Human and Machine Errors in Conversational Speech Transcription

Recent work in automatic recognition of conversational telephone speech (CTS) has achieved accuracy levels comparable to human transcribers, although there is some debate how to precisely quantify human performance on this task, using the NIST 2000 CTS evaluation set. This raises the question what systematic differences, if any, may be found differentiating human from machine transcription errors. In this paper we approach this question by comparing the output of our most accurate CTS recognition system to that of a standard speech transcription vendor pipeline. We find that the most frequent substitution, deletion and insertion error types of both outputs show a high degree of overlap. The only notable exception is that the automatic recognizer tends to confuse filled pauses ("uh") and backchannel acknowledgments ("uhhuh"). Humans tend not to make this error, presumably due to the distinctive and opposing pragmatic functions attached to these words. Furthermore, we quantify the correlation between human and machine errors at the speaker level, and investigate the effect of speaker overlap between training and test data. Finally, we report on an informal "Turing test" asking humans to discriminate between automatic and human transcription error cases

The Microsoft 2017 Conversational Speech Recognition System

We describe the 2017 version of Microsoft's conversational speech recognition system, in which we update our 2016 system with recent developments in neural-network-based acoustic and language modeling to further advance the state of the art on the Switchboard speech recognition task. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby subsets of acoustic models are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added a confusion network rescoring step after system combination. The resulting system yields a 5.1\% word error rate on the 2000 Switchboard evaluation set

The Spoken British National Corpus 2014:design, compilation and analysis

The ESRC-funded Centre for Corpus Approaches to Social Science at Lancaster University (CASS) and the English Language Teaching group at Cambridge University Press (CUP) have compiled a new, publicly-accessible corpus of spoken British English from the 2010s, known as the Spoken British National Corpus 2014 (Spoken BNC2014). The 11.5 million-word corpus, gathered solely in informal contexts, is the first freely-accessible corpus of its kind since the spoken component of the original British National Corpus (the Spoken BNC1994), which, despite its age, is still used as a proxy for present-day English in research today. This thesis presents a detailed account of each stage of the Spoken BNC2014’s construction, including its conception, design, transcription, processing and dissemination. It also demonstrates the research potential of the corpus, by presenting a diachronic analysis of ‘bad language’ in spoken British English, comparing the 1990s to the 2010s. The thesis shows how the research team struck a delicate balance between backwards compatibility with the Spoken BNC1994 and optimal practice in the context of compiling a new corpus. Although comparable with its predecessor, the Spoken BNC2014 is shown to represent innovation in approaches to the compilation of spoken corpora. This thesis makes several useful contributions to the linguistic research community. The Spoken BNC2014 itself should be of use to many researchers, educators and students in the corpus linguistics and English language communities and beyond. In addition, the thesis represents an example of good practice with regards to academic collaboration with a commercial stakeholder. Thirdly, although not a ‘user guide’, the methodological discussions and analysis presented in this thesis are intended to help the Spoken BNC2014 to be as useful to as many people, and for as many purposes, as possible

Fast transcription of unstructured audio recordings

URL to conference session list. Title is under heading: Wed-Ses1-P1: Phonetics, Phonology, cross-language comparisons, pathologyWe introduce a new method for human-machine collaborative speech transcription that is significantly faster than existing transcription methods. In this approach, automatic audio processing algorithms are used to robustly detect speech in audio recordings and split speech into short, easy to transcribe segments. Sequences of speech segments are loaded into a transcription interface that enables a human transcriber to simply listen and type, obviating the need for manually finding and segmenting speech or explicitly controlling audio playback. As a result, playback stays synchronized to the transcriber's speed of transcription. In evaluations using naturalistic audio recordings made in everyday home situations, the new method is up to 6 times faster than other popular transcription tools while preserving transcription quality

Clearing the transcription hurdle in dialect corpus building : the corpus of Southern Dutch dialects as case-study

This paper discusses how the transcription hurdle in dialect corpus building can be cleared. While corpus analysis has strongly gained in popularity in linguistic research, dialect corpora are still relatively scarce. This scarcity can be attributed to several factors, one of which is the challenging nature of transcribing dialects, given a lack of both orthographic norms for many dialects and speech technological tools trained on dialect data. This paper addresses the questions (i) how dialects can be transcribed efficiently and (ii) whether speech technological tools can lighten the transcription work. These questions are tackled using the Southern Dutch dialects (SDDs) as case study, for which the usefulness of automatic speech recognition (ASR), respeaking, and forced alignment is considered. Tests with these tools indicate that dialects still constitute a major speech technological challenge. In the case of the SDDs, the decision was made to use speech technology only for the word-level segmentation of the audio files, as the transcription itself could not be sped up by ASR tools. The discussion does however indicate that the usefulness of ASR and other related tools for a dialect corpus project is strongly determined by the sound quality of the dialect recordings, the availability of statistical dialect-specific models, the degree of linguistic differentiation between the dialects and the standard language, and the goals the transcripts have to serve

Chapter 2: The Original ToBI System and the Evolution of the ToBI Framework

In this chapter, the authors will try to identify the essential properties of a ToBI framework annotation system by describing the development and design of the original ToBI conventions. In this description, the authors will overview the general phonological theory and the specific theory of Mainstream American English intonation and prosody that the authors decided to incorporate in the original ToBI tags. The authors will also state the practical principles that led us to make the decisions that the authors did. The chapter is organised as follows. Section 2.2 briefly chronicles how the MAE_ToBI system came into being. Section 2.3 briefly describes the consensus account of English intonation and prosody on which the MAE_ToBI system is based. Section 2.4 catalogues the different components of a MAE_ToBI transcription and lists the salient rules which constrain the relationships between different components. This section also expands upon the theoretical foundations and practical consequences of adopting the general structure of multiple labelling tiers, and particularly the separation of the labels for tones from the labels for indexing prosodic boundary strength. Section 2.5 then describes some of the extensions of the basic ToBI tiers that have been adopted by some sites. This section also compares our decisions about the number of tiers and about inter-tier constraints with the analogous decisions for some of the other ToBI systems described in this book. Section 2.6 discusses the status of the symbolic labels relative to the continuous phonetic records that are also an obligatory component of the MAE_ToBI transcription. Section 2.7 then closes by listing several open research questions that the authors would like to see addressed by MAE_ToBI users and the larger ToBI community