arXiv subjects

Iroro Orife

Publications and source records attributed to Iroro Orife.

21 records · Page 2Linked to original sources

arXiv2018

Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yorùbá Language Text

Yorùbá is a widely spoken West African language with a writing system rich in tonal and orthographic diacritics. With very few exceptions, diacritics are omitted from electronic texts, due to limited device and application support. Diacritics provide morphological information, are crucial for lexical disambiguation, pronunciation and are vital for any Yorùbá text-to-speech (TTS), automatic speech recognition (ASR) and natural language processing (NLP) tasks. Reframing Automatic Diacritic Restoration (ADR) as a machine translation task, we experiment with two different attentive Sequence-to-Sequence neural models to process undiacritized text. On our evaluation dataset, this approach produces diacritization error rates of less than 5%. We have released pre-trained models, datasets and source-code as an open-source project to advance efforts on Yorùbá language technology.

cs.CL↗

arXiv2017

Semi-Supervised Model Training for Unbounded Conversational Speech Recognition

For conversational large-vocabulary continuous speech recognition (LVCSR) tasks, up to about two thousand hours of audio is commonly used to train state of the art models. Collection of labeled conversational audio however, is prohibitively expensive, laborious and error-prone. Furthermore, academic corpora like Fisher English (2004) or Switchboard (1992) are inadequate to train models with sufficient accuracy in the unbounded space of conversational speech. These corpora are also timeworn due to dated acoustic telephony features and the rapid advancement of colloquial vocabulary and idiomatic speech over the last decades. Utilizing the colossal scale of our unlabeled telephony dataset, we propose a technique to construct a modern, high quality conversational speech training corpus on the order of hundreds of millions of utterances (or tens of thousands of hours) for both acoustic and language model training. We describe the data collection, selection and training, evaluating the results of our updated speech recognition system on a test corpus of 7K manually transcribed utterances. We show relative word error rate (WER) reductions of {35%, 19%} on {agent, caller} utterances over our seed model and 5% absolute WER improvements over IBM Watson STT on this conversational speech task.

cs.CL↗

arXiv2017

Riddim: A Rhythm Analysis and Decomposition Tool Based On Independent Subspace Analysis

The goal of this thesis was to implement a tool that, given a digital audio input, can extract and represent rhythm and musical time. The purpose of the tool is to help develop better models of rhythm for real-time computer based performance and composition. This analysis tool, Riddim, uses Independent Subspace Analysis (ISA) and a robust onset detection scheme to separate and detect salient rhythmic and timing information from different sonic sources within the input. This information is then represented in a format that can be used by a variety of algorithms that interpret timing information to infer rhythmic and musical structure. A secondary objective of this work is a "proof of concept" as a non-real-time rhythm analysis system based on ISA. This is a necessary step since ultimately it is desirable to incorporate this functionality in a real-time plug-in for live performance and improvisation.

cs.SD↗