The high cost of data acquisition makes Automatic Speech Recognition (ASR)
model training problematic for most existing languages, including languages
that do not even have a written script, or for which the phone inventories
remain unknown. Past works explored multilingual training, transfer learning,
as well as zero-shot learning in order to build ASR systems for these
low-resource languages. While it has been shown that the pooling of resources
from multiple languages is helpful, we have not yet seen a successful
application of an ASR model to a language unseen during training. A crucial
step in the adaptation of ASR from seen to unseen languages is the creation of
the phone inventory of the unseen language. The ultimate goal of our work is to
build the phone inventory of a language unseen during training in an
unsupervised way without any knowledge about the language. In this paper, we 1)
investigate the influence of different factors (i.e., model architecture,
phonotactic model, type of speech representation) on phone recognition in an
unknown language; 2) provide an analysis of which phones transfer well across
languages and which do not in order to understand the limitations of and areas
for further improvement for automatic phone inventory creation; and 3) present
different methods to build a phone inventory of an unseen language in an
unsupervised way. To that end, we conducted mono-, multi-, and crosslingual
experiments on a set of 13 phonetically diverse languages and several in-depth
analyses. We found a number of universal phone tokens (IPA symbols) that are
well-recognized cross-linguistically. Through a detailed analysis of results,
we conclude that unique sounds, similar sounds, and tone languages remain a
major challenge for phonetic inventory discovery.

本文研究了如何在没有任何语言学知识的情况下，以单语、多语和跨语言的方式构建从未见过的语言的电话清单，并对 13 种语言进行了交叉语言分析，发现少数普遍的电话标记可在跨语言中很好地识别，但独特的声音、类似的声音和语调语言仍然是语音清单发现的主要障碍。