# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
## 3 Related Work
### Speech Segmentation  
[PERSON] et al. [25] developed an ASR system for automatic speech transcription of broadcast news (BN) in Portuguese language. To develop the system, the authors followed a hybrid approach combining hidden Markov model (HMM) and multi-layer perceptron (MLP). The system was tested with a 29-minute test set speech and compared with 3 transcription results. The first test was done on 241 manually transcribed sentences, and the second by considering the whole program as one sentence where no preprocessing was made, while the last test set was segmented automatically to produce 366 sentences. The recorded word error rate (WER) for the first, second, and last test sets were 26.9, 27.1, and 29.0, respectively. [PERSON] and [PERSON] [22] has developed a sentence-level automatic speech segmentation system for Amharic, which is used to segment the spoken speech into sentences. To implement the sentence-like segmentation, the author used two approaches. In the first approach, an automatic tool for segmenting and labeling Amharic speech data were used. In this approach, preprocessing rule-based segmentation using Audacity software, was applied to segment an audio file and given to the different acoustic models. Then, the audio file with its respective transcribed file was given to the Forced aligner, and the segmentation result was displayed. In the second approach, the author used energy and F0 features combined with seven prosodic features (rate-of-speech, volume change rate, pause, succeeding and preceding sentence duration, succeeding and preceding pause duration, and rate-of-speech duration) to detect sentence boundaries. Then, adaBoost algorithms were used to check the performance and accuracy of the supervised classifier. Following the two different approaches, the author found that the results of the first approach (rule-based) were better than the second approach. [PERSON] et al. [26] compared the relative performance of ASR systems developed using automatically and manually transcribed speech corpora. They used two sets of manual transcriptions and five sets of automatic transcriptions (Google Cloud, IBM Watson, Microsoft Azure, Trint, and YouTube). The obtained results showed that manual transcription is better than all other transcriptions (Google Cloud, IBM Watson, Microsoft Azure, Trint, and YouTube), even though YouTube offers accurate transcription compared to others. [PERSON] et al. [10] made an experiment on sub-word unit syllable-like and morpheme-like units for different languages. The aim of their research was on how to reduce OOV keywords that are generated by the ASR. The researchers compared Syllable-based units and Morpheme-based units (two approaches) for spoken term detection for OOV for Amharic,Guarani (official languages of Paraguay), Igbo (spoken in southeastern Nigeria), Javanese (spoken in Malaysia, the Netherlands, and Singapore), Dholuo (spoken in southwestern Kenya), Mongolian (spoken in Mongolia), and Pashto (Eastern Iranian) languages. The corpora used in their experiments were the \"Full language packs\", which were distributed in the fourth year of the intelligence advanced research projects activity (IARPA) project BA-BEL and each contained about 40 h of training data for all languages. Kaldi was used as a speech recognition toolkit. From the results they obtain when comparing the OOV results, whether to use syllables or morphemes becomes a language-specific choice. For Amharic, Dholuo, and Pashto, the morpheme-based OOV results were found to be best, while for the rest of the languages, syllable-based OOV results were slightly better (Guarani, Igbo, Javanese) to significantly better. [PERSON] and [PERSON] [27] segmented the speech signal into silence, voiced, and unvoiced regions. To achieve their research objective, they propose an algorithm that is fast and simple.