# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
## 7 Conclusions  
Searching for the location of a particular word with its respective time interval is a challenging task, particularly for a language such as Amharic, which has distinguishing characteristics such as glottal, palatal, and labialized consonants. This study aimed to investigate the development of a system that locates the time interval of a text word within an Amharic speech. To select the optimal segmentation to be applied for the development of text-based STD, ASR systems were developed using corpus from the spiritual domain. The performance of the systems was evaluated by test speech that is segmented using different segmentation methods: manual, phrase/word-like automatic, and sentence-like automatic. We found that the performance of the systems on sentence-like segmented test speech was much more similar to that of the manually segmented test speech. Therefore, we have proposed Amharic speech search using text word query (ASSTWQ) based on automatic sentence-like segmented test speech and using a previously developed speech corpus, which is in the broadcast news domain, together with the in-domain using the Bible domain speech corpus. The result showed that using the same domain for both the test and training speech corpus for LVCSR development performs better than using different domains. However, we also went further to check the performance of LVCSR by combining the training text corpus from the broadcast domain and the Bible text by interpolating the LMs and using the Bible as a test speech. We found that combining the LMs of two different domains showed better results than training LVCSR from one domain and testing with a speech from another domain. Finally, the developed ASSTWQ was tested with different query words that exactly locate the time interval in which the query text was located. In the future, this work can be extended in three ways. First, the technique of comparing the automatically and manually segmented speech can be applied to the training speech corpus by using the same methodology and technique. Second, searching on the text-based \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Domains** & **Duration** & **Segmentation Time** & **Decoding Time** \\ \hline \multirow{2}{*}{1} & Broadcast (Amharic news speech file) & \multirow{2}{*}{00:07:59} & \multirow{2}{*}{00:00:49} & \multirow{2}{*}{00:12:30} \\  & Spiritual (Amharic Bible speech file) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Efficiency of ASSTWQ. STD can be done using phrase- and sentence-like context searching, which can handle homophones. Finally, since the Amharic language is a morphologically rich language, we extend the STD search using steamers both on the query word and the transcribed text to improve the effectiveness of the text-based STD. Conceptualization, S.T.A. and G.M.B. ; methodology, S.T.A., T.A.A. and A.M.S. ; software, S.T.A. and G.M.B. ; validation, S.T.A. ; writing\(-\)original draft preparation, G.M.B. and T.A.A. ; writing\(-\)review and editing, T.A.A. All authors have read and agreed to the published version of the manuscript. This work was supported by Postdoctoral Foundation of Zhejiang Normal University under Grant ZC304021941. Not applicable. * [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] _Searching Speech Databases: Features, Techniques and Evaluation Measures_; Springer: Cham, Switzerland, 2018. * [PERSON] and [PERSON] (2012) [PERSON]; [PERSON] Spoken content retrieval: A survey of techniques and technologies. _Found. Trends Inf. Retr._**2012**, \(5\), 235-422. [CrossRef]
* [PERSON] (2015) [PERSON] Improving Spoken Term Detection Using Complementary Information. Ph.D. Thesis, Queensland University of Technology, Brisbane, QLD, Australia, 2015. * [PERSON] (2019) [PERSON] Keyword Search for Low Resource Languages. Ph.D. Thesis, Bogazici University, Istanbul, Turkey, 2017. * [PERSON] et al.