This study is aimed at investigating whether automatic phonetic transcription procedures can approximate manual transcriptions typically delivered with contemporary large speech corpora. To this end, ten automatic procedures were used to generate a broad phonetic transcription of well-prepared speech (read-aloud texts) and spontaneous speech (telephone dialogues) from the Spoken Dutch Corpus. The resulting transcriptions were compared to manually verified phonetic transcriptions from the same corpus.

Abstract—With the skyrocketing popularity of mobile devices, new processing methods tailored to a specific application have become necessary for low-resource systems. This work presents a high-speed, low-resource speech recognition system using custom arithmetic units, where all system variables are represented by integer indices and all arithmetic operations are replaced by hardware-based table lookups. To this end, several reordering and rescaling techniques, including two accumulation structures for Gaussian evaluation and a novel method for the normalization of Viterbi search scores, are proposed to ensure low entropy for all variables. Furthermore, a discriminatively inspired distortion measure is investigated for scalar quantization of forward probabilities to maximize the recognition rate. Finally, heuristic algorithms are explored to optimize system-wide resource allocation. Our best bit-width allocation scheme only requires 59 kB of ROMs to hold the lookup tables, and its recognition performance with various vocabulary sizes in both clean and noisy conditions is nearly as good as that of a system using a 32-bit floating-point unit. Simulations on various architectures show that, on most modern processor designs, we can expect a cycle-count speedup of at least three times over systems with floating-point units. Additionally, the memory bandwidth is reduced by over 70 % and the offline storage for model parameters is reduced by 80%. Index Terms—Alpha recursion, bit-width allocation, custom arithmetic, discriminative distortion measure, forward probability normalization and scaling, high speed, low resource, normalization, quantization, speech recognition. I.

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