Blind Segmentation of Speech Using Non-Linear Filtering Methods
Okko Johannes Räsänen, Unto Kalervo Laine, Toomas Altosaar · InTech eBooks · 2011
Automated segmentation of speech into phone-sized units has been a subject of study for over 30 years, as it plays a central role in many speech processing and ASR applications. While segmentation by hand is relatively precise, it is also extremely laborious and tedious. This is one reason why automated methods are widely utilized. For example, phonetic analysis of speech (Mermelstein, 1975), audio content classification (Zhang & Kuo, 1999), and word recognition (Antal, 2004) utilize segmentation for dividing continuous audio signals into discrete, non-overlapping units in order to provide structural descriptions for the different parts of a processed signal. In the field of automatic segmentation of speech, the best results have so far been achieved with semi-automatic HMMs that require prior training (see, e.g., Makhoul & Schwartz, 1994). Algorithms using additional linguistic information like phonetic annotation during the segmentation process are often also effective (e.g., Hemert, 1991). The use of these types of algorithms is well justified for several different purposes, but extensive training may not always be possible, nor may adequately rich descriptions of speech material be available, for instance, in real-time applications. Training of the algorithms also imposes limitations to the material that can be segmented effectively, with the results being highly dependent on, e.g., the language and vocabulary of the training and target material. Therefore, several researchers have concurrently worked on blind speech segmentation methods that do not require any external or prior knowledge regarding the speech to be segmented (Almpanidis & Kotropoulos, 2008; Aversano et al., 2001; Cherniz et al., 2007; Esposito & Aversano, 2005; Estevan et al., 2007; Sharma & Mammone, 1996). These so called blind segmentation algorithms have many potential applications in the field of speech processing that are complementary to supervised segmentation, since they do not need to be trained extensively on carefully prepared speech material. As an important property, blind algorithms do not necessarily make assumptions about underlying signal conditions whereas in trained algorithms possible mismatches between training data and processed input cause problems and errors in segmentation, e.g., due to changes in background noise conditions or microphone properties. Blind methods also provide a valuable tool for investigating speech from a basic level such as phonetic research, they are language independent, and they can be used as a processing step in self-learning agents attempting to make sense of sensory input where externally supplied linguistic knowledge cannot be used (e.g., Rasanen & Driesen, 2009; Rasanen et al., 2008).