In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Q: We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset. 
 Question: How big is LibriSpeech dataset?

A: 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset
****
Q: We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. 
 Question: What cluster identification method is used in this paper?

A: A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18
****
Q: The used classifiers are Support Vector Machine (SVM), Logistic regression (Log.Reg), Random Forest (RF) and gradient boosting (XGB). 
 Question: What classical machine learning algorithms are used?

A:
Support Vector Machine (SVM) Logistic regression (Log.Reg) Random Forest (RF) gradient boosting (XGB)
****