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In this paper, we describe several techniques for improving the acoustic and language model of an automatic speech recognition (ASR) system operating on code-switching (CS) speech. We focus on the recognition of Frisian-Dutch radio broadcasts where one of the mixed languages, namely Frisian, is an underresourced language. In previous work, we have proposed several automatic transcription strategies for CS speech to increase the amount of available training speech data. In this work, we explore how the acoustic modeling (AM) can benefit from monolingual speech data belonging to the high-resourced mixed language. For this purpose, we train state-of-the-art AMs, which were ineffective due to lack of training data, on a significantly increased amount of CS speech and monolingual Dutch speech. Moreover, we improve the language model (LM) by creating code-switching text, which is in practice almost nonexistent, by (1) generating text using recurrent LMs trained on the transcriptions of the training CS speech data, (2) adding the transcriptions of the automatically transcribed CS speech data and (3) translating Dutch text extracted from the transcriptions of a large Dutch speech corpora. We report significantly improved CS ASR performance due to the increase in the acoustic and textual training data.

Automatic speech recognition (ASR) of code-switching speech requires careful handling of unexpected language switches that may occur in a single utterance. In this paper, we investigate the feasibility of using multilingually trained deep neural networks (DNN) for the ASR of Frisian speech containing code-switches to Dutch with the aim of building a robust recognizer that can handle this phenomenon. For this purpose, we train several multilingual DNN models on Frisian and two closely related languages, namely English and Dutch, to compare the impact of single-step and two-step multilingual DNN training on the recognition and code-switching detection performance. We apply bilingual DNN retraining on both target languages by varying the amount of training data belonging to the higher-resourced target language (Dutch). The recognition results show that the multilingual DNN training scheme with an initial multilingual training step followed by bilingual retraining provides recognition performance comparable to an oracle baseline recognizer that can employ language-specific acoustic models. We further show that we can detect code-switches at the word level with an equal error rate of around 17% excluding the deletions due to ASR errors.

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In the FAME! project, we aim to develop an automatic speech recognition (ASR) system for Frisian-Dutch code-switching (CS) speech extracted from the archives of a local broadcaster with the ultimate goal of building a spoken document retrieval system. Unlike Dutch, Frisian is a low-resourced language with a very limited amount of manually annotated speech data. In this paper, we describe several automatic annotation approaches to enable using of a large amount of raw bilingual broadcast data for acoustic model training in a semi-supervised setting. Previously, it has been shown that the best-performing ASR system is obtained by two-stage multilingual deep neural network (DNN) training using 11 hours of manually annotated CS speech (reference) data together with speech data from other high-resourced languages. We compare the quality of transcriptions provided by this bilingual ASR system with several other approaches that use a language recognition system for assigning language labels to raw speech segments at the front-end and using monolingual ASR resources for transcription. We further investigate automatic annotation of the speakers appearing in the raw broadcast data by first labeling with (pseudo) speaker tags using a speaker diarization system and then linking to the known speakers appearing in the reference data using a speaker recognition system. These speaker labels are essential for speaker-adaptive training in the proposed setting. We train acoustic models using the manually and automatically annotated data and run recognition experiments on the development and test data of the FAME! speech corpus to quantify the quality of the automatic annotations. The ASR and CS detection results demonstrate the potential of using automatic language and speaker tagging in semi-supervised bilingual acoustic model training.

Most large speech corpora are delivered with a lexicon that contains a canonical transcription of every word in the orthographic transcription. Such a lexicon can be used for generating a hypothetical 'canonical' phonetic transcription from the orthography. In addition, time and money permitting, some speech corpora are provided with a manually verified broad phonetic transcription of at least part of the material. Since the manual verification of phonetic transcriptions is time-consuming and expensive, we investigated whether existing automatic transcription procedures and combinations of such procedures can offer a quick and cheap alternative for the generation of phonetic transcriptions like the manually verified transcriptions delivered with large speech corpora. In our study, we used ten automatic transcription procedures to generate a broad phonetic transcription of well-prepared speech (readaloud texts) and spontaneous speech (telephone dialogues) from the Spoken Dutch Corpus. The performance was assessed in terms of the number and the nature of the discrepancies between the emerging phonetic transcriptions and the corresponding manually verified phonetic transcriptions delivered with the Spoken Dutch Corpus. The resulting automatic transcriptions appeared to be comparable to the manually verified transcriptions.

In this paper, we investigate several automatic transcription schemes for using raw bilingual broadcast news data in semi-supervised bilingual acoustic model training. Specifically, we compare the transcription quality provided by a bilingual ASR system with another system performing language diarization at the front-end followed by two monolingual ASR systems chosen based on the assigned language label. Our research focuses on the Frisian-Dutch code-switching (CS) speech that is extracted from the archives of a local radio broadcaster. Using 11 hours of manually transcribed Frisian speech as a reference, we aim to increase the amount of available training data by using these automatic transcription techniques. By merging the manually and automatically transcribed data, we learn bilingual acoustic models and run ASR experiments on the development and test data of the FAME! speech corpus to quantify the quality of the automatic transcriptions. Using these acoustic models, we present speech recognition and CS detection accuracies. The results demonstrate that applying language diarization to the raw speech data to enable using the monolingual resources improves the automatic transcription quality compared to a baseline system using a bilingual ASR system.