  (Part of the abstract) In this thesis, we investigate the use of unsupervised
spoken term discovery in tackling this problem. Unsupervised spoken term
discovery aims to discover topic-related terminologies in a speech without
knowing the phonetic properties of the language and content. It can be further
divided into two parts: Acoustic segment modelling (ASM) and unsupervised
pattern discovery. ASM learns the phonetic structures of zero-resource language
audio with no phonetic knowledge available, generating self-derived "phonemes".
The audio are labelled with these "phonemes" to obtain "phoneme" sequences.
Unsupervised pattern discovery searches for repetitive patterns in the
"phoneme" sequences. The discovered patterns can be grouped to determine the
keywords of the audio. Multilingual neural network with bottleneck layer is
used for feature extraction. Experiments show that bottleneck features
facilitate the training of ASM compared to conventional features such as MFCC.
The unsupervised spoken term discovery system is experimented with online
lectures covering different topics by different speakers. It is shown that the
system learns the phonetic information of the language and can discover
frequent spoken terms that align with text transcription. By using information
retrieval technology such as word embedding and TFIDF, it is shown that the
discovered keywords can be further used for topic comparison.
