1. A method of enabling training of an acoustic model, the method comprising: dynamically shredding a speech corpus to produce text segments and depersonalized audio features corresponding to the text segments, the depersonalized audio features including filtered audio data remaining after speaker vocal characteristics and other audio characteristics have been removed, the speech corpus comprising a plurality of messages that each contain audio and corresponding text content, the shredding splitting each of the plurality of messages into strips, each strip comprising text segments and corresponding depersonalized audio features; mixing up the strips of the text segments and corresponding depersonalized audio features to produce strips mixed up in randomized order; and enabling a system to train an acoustic model using the strips mixed up in randomized order.
1. A method of enabling training of an acoustic model, the method comprising: dynamically shredding a speech corpus to produce text segments and depersonalized audio features corresponding to the text segments, the depersonalized audio features including filtered audio data remaining after speaker vocal characteristics and other audio characteristics have been removed, the speech corpus comprising a plurality of messages that each contain audio and corresponding text content, the shredding splitting each of the plurality of messages into strips, each strip comprising text segments and corresponding depersonalized audio features; mixing up the strips of the text segments and corresponding depersonalized audio features to produce strips mixed up in randomized order; and enabling a system to train an acoustic model using the strips mixed up in randomized order. 7. The method according to claim 1 , wherein dynamically shredding the speech corpus includes aligning text and audio of the speech corpus and splitting the text and audio at convenient places.