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The recognition of digital audio data manipulation is a challenge addressed by various fragile and content-fragile watermarking algorithms. But so far none of the approaches provides satisfying results with respect to manipulation detection. Especially distinguishing malicious attacks from allowed post production operations is still an open issue. In this paper we present an improvement of our content-based watermarking approach  for audio data authentication. We extend our embedding algorithm with an adaptive gap building for strengthening the content features used for authentication in low information areas. Our method is verified using several files of audio speech data.